♻ ★ NMPCB: A Lightweight and Safety-Critical Motion Control Framework for Ackermann Mobile Robot

Longze Zheng, Qinghe Liu

In multi-obstacle environments, real-time performance and safety in robot motion control have long been challenging issues, as conventional methods often struggle to balance the two. In this paper, we propose a novel motion control framework composed of a Neural network-based path planner and a Model Predictive Control (MPC) controller based on control Barrier function (NMPCB) . The planner predicts the next target point through a lightweight neural network and generates a reference trajectory for the controller. In the design of the controller, we introduce the dual problem of control barrier function (CBF) as the obstacle avoidance constraint, enabling it to ensure robot motion safety while significantly reducing computation time. The controller directly outputs control commands to the robot by tracking the reference trajectory. This framework achieves a balance between real-time performance and safety. We validate the feasibility of the framework through numerical simulations and real-world experiments.

♻ ★ Efficient Manipulation-Enhanced Semantic Mapping With Uncertainty-Informed Action Selection

Nils Dengler, Jesper Mücke, Rohit Menon, Maren Bennewitz

Service robots operating in cluttered human environments such as homes, offices, and schools cannot rely on predefined object arrangements and must continuously update their semantic and spatial estimates while dealing with possible frequent rearrangements. Efficient and accurate mapping under such conditions demands selecting informative viewpoints and targeted manipulations to reduce occlusions and uncertainty. In this work, we present a manipulation-enhanced semantic mapping framework for occlusion-heavy shelf scenes that integrates evidential metric-semantic mapping with reinforcement-learning-based next-best view planning and targeted action selection. Our method thereby exploits uncertainty estimates from Dirichlet and Beta distributions in the map prediction networks to guide both active sensor placement and object manipulation, focusing on areas with high uncertainty and selecting actions with high expected information gain. Furthermore, we introduce an uncertainty-informed push strategy that targets occlusion-critical objects and generates minimally invasive actions to reveal hidden regions by reducing overall uncertainty in the scene. The experimental evaluation shows that our framework enables to accurately map cluttered scenes, while substantially reducing object displacement and achieving a 95% reduction in planning time compared to the state-of-the-art, thereby realizing real-world applicability.

♻ ★ Co-Design of Soft Gripper with Neural Physics

Sha Yi, Xueqian Bai, Adabhav Singh, Jianglong Ye, Michael T Tolley, Xiaolong Wang

For robot manipulation, both the controller and end-effector design are crucial. Soft grippers are generalizable by deforming to different geometries, but designing such a gripper and finding its grasp pose remains challenging. In this paper, we propose a co-design framework that generates an optimized soft gripper's block-wise stiffness distribution and its grasping pose, using a neural physics model trained in simulation. We derived a uniform-pressure tendon model for a flexure-based soft finger, then generated a diverse dataset by randomizing both gripper pose and design parameters. A neural network is trained to approximate this forward simulation, yielding a fast, differentiable surrogate. We embed that surrogate in an end-to-end optimization loop to optimize the ideal stiffness configuration and best grasp pose. Finally, we 3D-print the optimized grippers of various stiffness by changing the structural parameters. We demonstrate that our co-designed grippers significantly outperform baseline designs in both simulation and hardware experiments. More info: http://yswhynot.github.io/codesign-soft/

♻ ★ NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving

Ren Xin, Hongji Liu, Xiaodong Mei, Wenru Liu, Maosheng Ye, Zhili Chen, Jun Ma

Integrating General Models (GMs) such as Large Language Models (LLMs), with Specialized Models (SMs) in autonomous driving tasks presents a promising approach to mitigating challenges in data diversity and model capacity of existing specialized driving models. However, this integration leads to problems of asynchronous systems, which arise from the distinct characteristics inherent in GMs and SMs. To tackle this challenge, we propose NetRoller, an adapter that incorporates a set of novel mechanisms to facilitate the seamless integration of GMs and specialized driving models. Specifically, our mechanisms for interfacing the asynchronous GMs and SMs are organized into three key stages. NetRoller first harvests semantically rich and computationally efficient representations from the reasoning processes of LLMs using an early stopping mechanism, which preserves critical insights on driving context while maintaining low overhead. It then applies learnable query embeddings, nonsensical embeddings, and positional layer embeddings to facilitate robust and efficient cross-modality translation. At last, it employs computationally efficient Query Shift and Feature Shift mechanisms to enhance the performance of SMs through few-epoch fine-tuning. Based on the mechanisms formalized in these three stages, NetRoller enables specialized driving models to operate at their native frequencies while maintaining situational awareness of the GM. Experiments conducted on the nuScenes dataset demonstrate that integrating GM through NetRoller significantly improves human similarity and safety in planning tasks, and it also achieves noticeable precision improvements in detection and mapping tasks for end-to-end autonomous driving. The code and models are available at https://github.com/Rex-sys-hk/NetRoller .

comment: This work has been submitted to the IEEE for possible publication

♻ ★ Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents

Tianmi Ma, Jiawei Du, Wenxin Huang, Wenjie Wang, Liang Xie, Xian Zhong, Joey Tianyi Zhou

Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks, yet their performance in dynamic, real-world financial environments remains underexplored. Existing approaches are limited to historical backtesting, where trading actions cannot influence market prices and agents train only on static data. To address this limitation, we present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive multi-agent trading and directly impact price dynamics. By simulating realistic bid-ask interactions, our platform enables training in scenarios that closely mirror live markets, thereby narrowing the gap between training and evaluation. Experiments reveal that LLMs struggle with numerical reasoning when given plain-text data, often overfitting to local patterns and recent values. In contrast, chart-based visualizations significantly enhance both numerical reasoning and trading performance. Furthermore, incorporating a reflection module yields additional improvements, especially with visual inputs. Evaluations on NASDAQ and CSI datasets demonstrate the superiority of our method, particularly under high volatility. All code and data are available at https://github.com/wekjsdvnm/Agent-Trading-Arena.

♻ ★ On Word-of-Mouth and Private-Prior Sequential Social Learning

Andrea Da Col, Cristian R. Rojas, Vikram Krishnamurthy

Social learning constitutes a fundamental framework for studying interactions among rational agents who observe each other's actions but lack direct access to individual beliefs. This paper investigates a specific social learning paradigm known as Word-of-Mouth (WoM), where a series of agents seeks to estimate the state of a dynamical system. The first agent receives noisy measurements of the state, while each subsequent agent relies solely on a degraded version of her predecessor's estimate. A defining feature of WoM is that the final agent's belief is publicly broadcast and subsequently adopted by all agents, in place of their own. We analyze this setting theoretically and through numerical simulations, noting that some agents benefit from using the belief of the last agent, while others experience performance deterioration.

comment: Accepted for publication at the 64th Conference on Decision and Control (CDC)

♻ ★ Data-Driven Abstraction and Synthesis for Stochastic Systems with Unknown Dynamics

Mahdi Nazeri, Thom Badings, Anne-Kathrin Schmuck, Sadegh Soudjani, Alessandro Abate

We study the automated abstraction-based synthesis of correct-by-construction control policies for stochastic dynamical systems with unknown dynamics. Our approach is to learn an abstraction from sampled data, which is represented in the form of a finite Markov decision process (MDP). In this paper, we present a data-driven technique for constructing finite-state interval MDP (IMDP) abstractions of stochastic systems with unknown nonlinear dynamics. As a distinguishing and novel feature, our technique only requires (1) noisy state-input-state observations and (2) an upper bound on the system's Lipschitz constant. Combined with standard model-checking techniques, our IMDP abstractions enable the synthesis of policies that satisfy probabilistic temporal properties (such as "reach-while-avoid") with a predefined confidence. Our experimental results show the effectiveness and robustness of our approach.

♻ ★ Incremental Collision Laws Based on the Bouc-Wen Model: Improved Collision Models and Further Results

Mihails Milehins, Dan B. Marghitu

In the article titled "The Bouc-Wen Model for Binary Direct Collinear Collisions of Convex Viscoplastic Bodies" and published in the Journal of Computational and Nonlinear Dynamics (Volume 20, Issue 6, June 2025), the authors studied mathematical models of binary direct collinear collisions of convex viscoplastic bodies that employed two incremental collision laws based on the Bouc-Wen differential model of hysteresis. It was shown that the models possess favorable analytical properties, and several model parameter identification studies were conducted, demonstrating that the models can accurately capture the nature of a variety of collision phenomena. In this article, the aforementioned models are augmented by modeling the effects of external forces as time-dependent inputs that belong to a certain function space. Furthermore, the range of the parameters under which the models possess favorable analytical properties is extended to several corner cases that were not considered in the prior publication. Finally, the previously conducted model parameter identification studies are extended, and an additional model parameter identification study is provided in an attempt to validate the ability of the augmented models to represent the effects of external forces.

comment: 12 pages, 4 figures, see https://gitlab.com/user9716869/EBWCM ; (v2-v6) various minor amendments; (v5) replaced the parameter identification study of Quinn (2004) with Villegas et al (2021) due to incompatibility of the proposed collision models with the experimental setup in Quinn (2004); arXiv admin note: text overlap with arXiv:2410.08147

♻ ★ Data-Driven Abstraction and Synthesis for Stochastic Systems with Unknown Dynamics

Mahdi Nazeri, Thom Badings, Anne-Kathrin Schmuck, Sadegh Soudjani, Alessandro Abate

We study the automated abstraction-based synthesis of correct-by-construction control policies for stochastic dynamical systems with unknown dynamics. Our approach is to learn an abstraction from sampled data, which is represented in the form of a finite Markov decision process (MDP). In this paper, we present a data-driven technique for constructing finite-state interval MDP (IMDP) abstractions of stochastic systems with unknown nonlinear dynamics. As a distinguishing and novel feature, our technique only requires (1) noisy state-input-state observations and (2) an upper bound on the system's Lipschitz constant. Combined with standard model-checking techniques, our IMDP abstractions enable the synthesis of policies that satisfy probabilistic temporal properties (such as "reach-while-avoid") with a predefined confidence. Our experimental results show the effectiveness and robustness of our approach.

♻ ★ Incremental Collision Laws Based on the Bouc-Wen Model: Improved Collision Models and Further Results

Mihails Milehins, Dan B. Marghitu

In the article titled "The Bouc-Wen Model for Binary Direct Collinear Collisions of Convex Viscoplastic Bodies" and published in the Journal of Computational and Nonlinear Dynamics (Volume 20, Issue 6, June 2025), the authors studied mathematical models of binary direct collinear collisions of convex viscoplastic bodies that employed two incremental collision laws based on the Bouc-Wen differential model of hysteresis. It was shown that the models possess favorable analytical properties, and several model parameter identification studies were conducted, demonstrating that the models can accurately capture the nature of a variety of collision phenomena. In this article, the aforementioned models are augmented by modeling the effects of external forces as time-dependent inputs that belong to a certain function space. Furthermore, the range of the parameters under which the models possess favorable analytical properties is extended to several corner cases that were not considered in the prior publication. Finally, the previously conducted model parameter identification studies are extended, and an additional model parameter identification study is provided in an attempt to validate the ability of the augmented models to represent the effects of external forces.

comment: 12 pages, 4 figures, see https://gitlab.com/user9716869/EBWCM ; (v2-v6) various minor amendments; (v5) replaced the parameter identification study of Quinn (2004) with Villegas et al (2021) due to incompatibility of the proposed collision models with the experimental setup in Quinn (2004); arXiv admin note: text overlap with arXiv:2410.08147

♻ ★ Adaptive Dead-Zone Dual Sliding Mode Observer for Reliable Electrochemical Model-Based SOC Estimation

Guangdi Hu, Keyi Liao, Jian Ye, Feng Guo

Accurate state of charge (SOC) estimation is critical for ensuring the safety, reliability, and efficiency of lithium-ion batteries in electric vehicles and energy storage systems. Electrochemical models provide high fidelity for SOC estimation but introduce challenges due to parameter variations, nonlinearities, and computational complexity. To address these issues, this paper proposes an adaptive dead-zone dual sliding mode observer(SMO) based on an improved electrochemical single-particle model. The algorithm integrates a state observer for SOC estimation and a parameter observer for online parameter adaptation. A Lyapunov-derived adaptive dead-zone is introduced to ensure stability, activating parameter updates only when the terminal voltage error lies within a rigorously defined bound. The proposed method was validated under constant-current and UDDS dynamic conditions. Results demonstrate that the adaptive dead-zone dual SMO achieves superior accuracy compared with conventional dual SMO and equivalent circuit model-based EKF methods, maintaining SOC estimation errors within 0.2% under correct initialization and below 1% under a 30% initial SOC error, with rapid convergence. Computational efficiency analysis further shows that the adaptive dead-zone dual sliding mode observer reduces execution time compared with the conventional dual SMO by limiting unnecessary parameter updates, highlighting its suitability for real-time battery management applications. Moreover, robustness under battery aging was confirmed using a cycle-aging model, where the adaptive dead-zone dual SMO maintained stable SOC estimation despite parameter drift. These findings indicate that the proposed method offers a reliable, accurate, and computationally efficient solution for SOC estimation.

comment: 36 pages, 5 figures

♻ ★ Benchmarking LLM Privacy Recognition for Social Robot Decision Making

Dakota Sullivan, Shirley Zhang, Jennica Li, Heather Kirkorian, Bilge Mutlu, Kassem Fawaz

While robots have previously utilized rule-based systems or probabilistic models for user interaction, the rapid evolution of large language models (LLMs) presents new opportunities to develop LLM-powered robots for enhanced human-robot interaction (HRI). To fully realize these capabilities, however, robots need to collect data such as audio, fine-grained images, video, and locations. As a result, LLMs often process sensitive personal information, particularly within private environments, such as homes. Given the tension between utility and privacy risks, evaluating how current LLMs manage sensitive data is critical. Specifically, we aim to explore the extent to which out-of-the-box LLMs are privacy-aware in the context of household robots. In this work, we present a set of privacy-relevant scenarios developed using the Contextual Integrity (CI) framework. We first surveyed users' privacy preferences regarding in-home robot behaviors and then examined how their privacy orientations affected their choices of these behaviors (N = 450). We then provided the same set of scenarios and questions to state-of-the-art LLMs (N = 10) and found that the agreement between humans and LLMs was generally low. To further investigate the capabilities of LLMs as potential privacy controllers, we implemented four additional prompting strategies and compared their results. We discuss the performance of the evaluated models as well as the implications and potential of AI privacy awareness in human-robot interaction.

comment: 18 pages, 7 figures. Dakota Sullivan and Shirley Zhang contributed equally to this work

♻ ★ Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids

Toru Lin, Kartik Sachdev, Linxi Fan, Jitendra Malik, Yuke Zhu

Learning generalizable robot manipulation policies, especially for complex multi-fingered humanoids, remains a significant challenge. Existing approaches primarily rely on extensive data collection and imitation learning, which are expensive, labor-intensive, and difficult to scale. Sim-to-real reinforcement learning (RL) offers a promising alternative, but has mostly succeeded in simpler state-based or single-hand setups. How to effectively extend this to vision-based, contact-rich bimanual manipulation tasks remains an open question. In this paper, we introduce a practical sim-to-real RL recipe that trains a humanoid robot to perform three challenging dexterous manipulation tasks: grasp-and-reach, box lift and bimanual handover. Our method features an automated real-to-sim tuning module, a generalized reward formulation based on contact and object goals, a divide-and-conquer policy distillation framework, and a hybrid object representation strategy with modality-specific augmentation. We demonstrate high success rates on unseen objects and robust, adaptive policy behaviors -- highlighting that vision-based dexterous manipulation via sim-to-real RL is not only viable, but also scalable and broadly applicable to real-world humanoid manipulation tasks.

comment: Published at CoRL 2025. Project page can be found at https://toruowo.github.io/recipe/

♻ ★ Self-Supervised Learning-Based Path Planning and Obstacle Avoidance Using PPO and B-Splines in Unknown Environments

Shahab Shokouhi, Oguzhan Oruc, May-Win Thein

This paper introduces SmartBSP, an advanced self-supervised learning framework for real-time path planning and obstacle avoidance in autonomous robotics navigating through complex environments. The proposed system integrates Proximal Policy Optimization (PPO) with Convolutional Neural Networks (CNN) and Actor-Critic architecture to process limited LIDAR inputs and compute spatial decision-making probabilities. The robot's perceptual field is discretized into a grid format, which the CNN analyzes to produce a spatial probability distribution. During the training process a nuanced cost function is minimized that accounts for path curvature, endpoint proximity, and obstacle avoidance. Simulations results in different scenarios validate the algorithm's resilience and adaptability across diverse operational scenarios. Subsequently, Real-time experiments, employing the Robot Operating System (ROS), were carried out to assess the efficacy of the proposed algorithm.

♻ ★ Morphologically Symmetric Reinforcement Learning for Ambidextrous Bimanual Manipulation

Zechu Li, Yufeng Jin, Daniel Ordonez Apraez, Claudio Semini, Puze Liu, Georgia Chalvatzaki

Humans naturally exhibit bilateral symmetry in their gross manipulation skills, effortlessly mirroring simple actions between left and right hands. Bimanual robots-which also feature bilateral symmetry-should similarly exploit this property to perform tasks with either hand. Unlike humans, who often favor a dominant hand for fine dexterous skills, robots should ideally execute ambidextrous manipulation with equal proficiency. To this end, we introduce SYMDEX (SYMmetric DEXterity), a reinforcement learning framework for ambidextrous bi-manipulation that leverages the robot's inherent bilateral symmetry as an inductive bias. SYMDEX decomposes complex bimanual manipulation tasks into per-hand subtasks and trains dedicated policies for each. By exploiting bilateral symmetry via equivariant neural networks, experience from one arm is inherently leveraged by the opposite arm. We then distill the subtask policies into a global ambidextrous policy that is independent of the hand-task assignment. We evaluate SYMDEX on six challenging simulated manipulation tasks and demonstrate successful real-world deployment on two of them. Our approach strongly outperforms baselines on complex task in which the left and right hands perform different roles. We further demonstrate SYMDEX's scalability by extending it to a four-arm manipulation setup, where our symmetry-aware policies enable effective multi-arm collaboration and coordination. Our results highlight how structural symmetry as inductive bias in policy learning enhances sample efficiency, robustness, and generalization across diverse dexterous manipulation tasks.

♻ ★ Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination IROS

Saad Abdul Ghani, Zizhao Wang, Peter Stone, Xuesu Xiao

This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic obstacles. Classical planners struggle with dense, unpredictable obstacles due to limited computation, while learning-based planners face challenges in acquiring high- quality demonstrations for imitation learning or dealing with exploration inefficiencies in reinforcement learning. Building on Learning from Hallucination (LfH), which synthesizes training data from past successful navigation experiences in simpler environments, Dyna-LfLH incorporates dynamic obstacles by generating them through a learned latent distribution. This enables efficient and safe motion planner training. We evaluate Dyna-LfLH on a ground robot in both simulated and real environments, achieving up to a 25% improvement in success rate compared to baselines.

comment: Accepted at International Conference on Intelligent Robots and Systems (IROS) 2025 Hangzhou, China

♻ ★ SoK: Cybersecurity Assessment of Humanoid Ecosystem

Priyanka Prakash Surve, Asaf Shabtai, Yuval Elovici

Humanoids are progressing toward practical deployment across healthcare, industrial, defense, and service sectors. While typically considered cyber-physical systems (CPSs), their dependence on traditional networked software stacks (e.g., Linux operating systems), robot operating system (ROS) middleware, and over-the-air update channels, creates a distinct security profile that exposes them to vulnerabilities conventional CPS models do not fully address. Prior studies have mainly examined specific threats, such as LiDAR spoofing or adversarial machine learning (AML). This narrow focus overlooks how an attack targeting one component can cascade harm throughout the robot's interconnected systems. We address this gap through a systematization of knowledge (SoK) that takes a comprehensive approach, consolidating fragmented research from robotics, CPS, and network security domains. We introduce a seven-layer security model for humanoid robots, organizing 39 known attacks and 35 defenses across the humanoid ecosystem-from hardware to human-robot interaction. Building on this security model, we develop a quantitative 39x35 attack-defense matrix with risk-weighted scoring, validated through Monte Carlo analysis. We demonstrate our method by evaluating three real-world robots: Pepper, G1 EDU, and Digit. The scoring analysis revealed varying security maturity levels, with scores ranging from 39.9% to 79.5% across the platforms. This work introduces a structured, evidence-based assessment method that enables systematic security evaluation, supports cross-platform benchmarking, and guides prioritization of security investments in humanoid robotics.

♻ ★ SAVOR: Skill Affordance Learning from Visuo-Haptic Perception for Robot-Assisted Bite Acquisition

Zhanxin Wu, Bo Ai, Tom Silver, Tapomayukh Bhattacharjee

Robot-assisted feeding requires reliable bite acquisition, a challenging task due to the complex interactions between utensils and food with diverse physical properties. These interactions are further complicated by the temporal variability of food properties-for example, steak becomes firm as it cools even during a meal. To address this, we propose SAVOR, a novel approach for learning skill affordances for bite acquisition-how suitable a manipulation skill (e.g., skewering, scooping) is for a given utensil-food interaction. In our formulation, skill affordances arise from the combination of tool affordances (what a utensil can do) and food affordances (what the food allows). Tool affordances are learned offline through calibration, where different utensils interact with a variety of foods to model their functional capabilities. Food affordances are characterized by physical properties such as softness, moisture, and viscosity, initially inferred through commonsense reasoning using a visually-conditioned language model and then dynamically refined through online multi-modal visuo-haptic perception using SAVOR-Net during interaction. Our method integrates these offline and online estimates to predict skill affordances in real time, enabling the robot to select the most appropriate skill for each food item. Evaluated on 20 single-item foods and 10 in-the-wild meals, our approach improves bite acquisition success rate by 13% over state-of-the-art (SOTA) category-based methods (e.g. use skewer for fruits). These results highlight the importance of modeling interaction-driven skill affordances for generalizable and effective robot-assisted bite acquisition. Website: https://emprise.cs.cornell.edu/savor/

comment: Conference on Robot Learning, Oral

♻ ★ Wavelet Policy: Imitation Policy Learning in the Scale Domain with Wavelet Transforms

Changchuan Yang, Yuhang Dong, Guanzhong Tian, Haizhou Ge, Hongrui Zhu

Recent imitation learning policies, often framed as time series prediction tasks, directly map robotic observations into the action space, such as high-dimensional visual data and proprioception. When deploying at the edge, we found the underutilization of frequency domain analysis in robotic manipulation trajectory prediction leads to neglecting the inherent rhythm information embedded within action sequences, resulting in errors at critical moments. To address this, we reframe imitation learning policies through the lens of time-scale domain and introduce the Wavelet Policy. This novel approach employs wavelet transforms (WT) and new Features Extractor (FE) for feature preprocessing and extracts multi-scale features using the Single Encoder to Multiple Decoder (SE2MD) architecture. Furthermore, to enhance feature mapping in the scale domain and appropriately increase model capacity, we introduce a Learnable Scale Domain Filter (LSDF) after each decoder, improving adaptability under different visual conditions. Our results show that the Wavelet Policy maintaining a comparable parameter count outperforms SOTA end-to-end methods on four challenging simulation robotic arm tasks and real tasks, especially at critical moments and remote settings simultaneously. We release the source code and model checkpoint of simulation task at https://github.com/lurenjia384/Wavelet_Policy.

♻ ★ RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms

Ziyao Wang, Rongpeng Li, Sizhao Li, Yuming Xiang, Haiping Wang, Zhifeng Zhao, Honggang Zhang

Intelligent control of Unmanned Aerial Vehicles (UAVs) swarms has emerged as a critical research focus, and it typically requires the swarm to navigate effectively while avoiding obstacles and achieving continuous coverage over multiple mission targets. Although traditional Multi-Agent Reinforcement Learning (MARL) approaches offer dynamic adaptability, they are hindered by the semantic gap in numerical communication and the rigidity of homogeneous role structures, resulting in poor generalization and limited task scalability. Recent advances in Large Language Model (LLM)-based control frameworks demonstrate strong semantic reasoning capabilities by leveraging extensive prior knowledge. However, due to the lack of online learning and over-reliance on static priors, these works often struggle with effective exploration, leading to reduced individual potential and overall system performance. To address these limitations, we propose a Role-Adaptive LLM-Driven Yoked navigation algorithm RALLY. Specifically, we first develop an LLM-driven semantic decision framework that uses structured natural language for efficient semantic communication and collaborative reasoning. Afterward, we introduce a dynamic role-heterogeneity mechanism for adaptive role switching and personalized decision-making. Furthermore, we propose a Role-value Mixing Network (RMIX)-based assignment strategy that integrates LLM offline priors with MARL online policies to enable semi-offline training of role selection strategies. Experiments in the Multi-Agent Particle Environment (MPE) environment and a Software-In-The-Loop (SITL) platform demonstrate that RALLY outperforms conventional approaches in terms of task coverage, convergence speed, and generalization, highlighting its strong potential for collaborative navigation in agentic multi-UAV systems.

♻ ★ General agents contain world models ICML 2025

Jonathan Richens, David Abel, Alexis Bellot, Tom Everitt

Are world models a necessary ingredient for flexible, goal-directed behaviour, or is model-free learning sufficient? We provide a formal answer to this question, showing that any agent capable of generalizing to multi-step goal-directed tasks must have learned a predictive model of its environment. We show that this model can be extracted from the agent's policy, and that increasing the agents performance or the complexity of the goals it can achieve requires learning increasingly accurate world models. This has a number of consequences: from developing safe and general agents, to bounding agent capabilities in complex environments, and providing new algorithms for eliciting world models from agents.

comment: Accepted ICML 2025. Typos corrected

♻ ★ Force Myography based Torque Estimation in Human Knee and Ankle Joints ICRA

Charlotte Marquardt, Arne Schulz, Miha Dezman, Gunther Kurz, Thorsten Stein, Tamim Asfour

The online adaptation of exoskeleton control based on muscle activity sensing offers a promising approach to personalizing exoskeleton behavior based on the user's biosignals. While electromyography (EMG)-based methods have demonstrated improvements in joint torque estimation, EMG sensors require direct skin contact and extensive post-processing. In contrast, force myography (FMG) measures normal forces resulting from changes in muscle volume due to muscle activity. We propose an FMG-based method to estimate knee and ankle joint torques by integrating joint angles and velocities with muscle activity data. We learn a model for joint torque estimation using Gaussian process regression (GPR). The effectiveness of the proposed FMG-based method is validated on isokinetic motions performed by ten participants. The model is compared to a baseline model that uses only joint angle and velocity, as well as a model augmented by EMG data. The results indicate that incorporating FMG into exoskeleton control can improve the estimation of joint torque for the ankle and knee joints in novel task characteristics within a single participant. Although the findings suggest that this approach may not improve the generalizability of estimates between multiple participants, they highlight the need for further research into its potential applications in exoskeleton control.

comment: This file corresponds to the manuscript presented at the IEEE International Conference on Robotics and Automation (ICRA), May 2025

♻ ★ Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey

Rui Shao, Wei Li, Lingsen Zhang, Renshan Zhang, Zhiyang Liu, Ran Chen, Liqiang Nie

Robotic manipulation, a key frontier in robotics and embodied AI, requires precise motor control and multimodal understanding, yet traditional rule-based methods fail to scale or generalize in unstructured, novel environments. In recent years, Vision-Language-Action (VLA) models, built upon Large Vision-Language Models (VLMs) pretrained on vast image-text datasets, have emerged as a transformative paradigm. This survey provides the first systematic, taxonomy-oriented review of large VLM-based VLA models for robotic manipulation. We begin by clearly defining large VLM-based VLA models and delineating two principal architectural paradigms: (1) monolithic models, encompassing single-system and dual-system designs with differing levels of integration; and (2) hierarchical models, which explicitly decouple planning from execution via interpretable intermediate representations. Building on this foundation, we present an in-depth examination of large VLM-based VLA models: (1) integration with advanced domains, including reinforcement learning, training-free optimization, learning from human videos, and world model integration; (2) synthesis of distinctive characteristics, consolidating architectural traits, operational strengths, and the datasets and benchmarks that support their development; (3) identification of promising directions, including memory mechanisms, 4D perception, efficient adaptation, multi-agent cooperation, and other emerging capabilities. This survey consolidates recent advances to resolve inconsistencies in existing taxonomies, mitigate research fragmentation, and fill a critical gap through the systematic integration of studies at the intersection of large VLMs and robotic manipulation. We provide a regularly updated project page to document ongoing progress: https://github.com/JiuTian-VL/Large-VLM-based-VLA-for-Robotic-Manipulation

comment: Project Page: https://github.com/JiuTian-VL/Large-VLM-based-VLA-for-Robotic-Manipulation

♻ ★ Temporal Preference Optimization for Long-Form Video Understanding

Rui Li, Xiaohan Wang, Yuhui Zhang, Orr Zohar, Zeyu Wang, Serena Yeung-Levy

Despite significant advancements in video large multimodal models (video-LMMs), achieving effective temporal grounding in long-form videos remains a challenge for existing models. To address this limitation, we propose Temporal Preference Optimization (TPO), a novel post-training framework designed to enhance the temporal grounding capabilities of video-LMMs through preference learning. TPO adopts a self-training approach that enables models to differentiate between well-grounded and less accurate temporal responses by leveraging curated preference datasets at two granularities: localized temporal grounding, which focuses on specific video segments, and comprehensive temporal grounding, which captures extended temporal dependencies across entire video sequences. By optimizing on these preference datasets, TPO significantly enhances temporal understanding while reducing reliance on manually annotated data. Extensive experiments on three long-form video understanding benchmarks--LongVideoBench, MLVU, and Video-MME--demonstrate the effectiveness of TPO across two state-of-the-art video-LMMs. Notably, LLaVA-Video-TPO establishes itself as the leading 7B model on the Video-MME benchmark, underscoring the potential of TPO as a scalable and efficient solution for advancing temporal reasoning in long-form video understanding. Project page: https://ruili33.github.io/tpo_website.

♻ ★ NarraGuide: an LLM-based Narrative Mobile Robot for Remote Place Exploration

Yaxin Hu, Arissa J. Sato, Jingxin Du, Chenming Ye, Anjun Zhu, Pragathi Praveena, Bilge Mutlu

Robotic telepresence enables users to navigate and experience remote environments. However, effective navigation and situational awareness depend on users' prior knowledge of the environment, limiting the usefulness of these systems for exploring unfamiliar places. We explore how integrating location-aware LLM-based narrative capabilities into a mobile robot can support remote exploration. We developed a prototype system, called NarraGuide, that provides narrative guidance for users to explore and learn about a remote place through a dialogue-based interface. We deployed our prototype in a geology museum, where remote participants (n=20) used the robot to tour the museum. Our findings reveal how users perceived the robot's role, engaged in dialogue in the tour, and expressed preferences for bystander encountering. Our work demonstrates the potential of LLM-enabled robotic capabilities to deliver location-aware narrative guidance and enrich the experience of exploring remote environments.

♻ ★ ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface

Fangchen Liu, Chuanyu Li, Yihua Qin, Jing Xu, Pieter Abbeel, Rui Chen

Tactile information plays a crucial role for humans and robots to interact effectively with their environment, particularly for tasks requiring the understanding of contact properties. Solving such dexterous manipulation tasks often relies on imitation learning from demonstration datasets, which are typically collected via teleoperation systems and often demand substantial time and effort. To address these challenges, we present ViTaMIn, an embodiment-free manipulation interface that seamlessly integrates visual and tactile sensing into a hand-held gripper, enabling data collection without the need for teleoperation. Our design employs a compliant Fin Ray gripper with tactile sensing, allowing operators to perceive force feedback during manipulation for more intuitive operation. Additionally, we propose a multimodal representation learning strategy to obtain pre-trained tactile representations, improving data efficiency and policy robustness. Experiments on seven contact-rich manipulation tasks demonstrate that ViTaMIn significantly outperforms baseline methods, demonstrating its effectiveness for complex manipulation tasks.

♻ ★ DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes EMNLP 2025

Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman Pan, Yangqiu Song, Dong Yu

Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding. However, their potential to comprehend embodied environments and navigate within them remains underexplored. In this work, we first study the challenge of open-vocabulary object navigation by introducing DivScene, a large-scale dataset with 4,614 houses across 81 scene types and 5,707 kinds of target objects. Our dataset provides a much greater diversity of target objects and scene types than existing datasets, enabling a comprehensive task evaluation. We evaluated various methods with LVLMs and LLMs on our dataset and found that current models still fall short of open-vocab object navigation ability. Then, we fine-tuned LVLMs to predict the next action with CoT explanations. We observe that LVLM's navigation ability can be improved substantially with only BFS-generated shortest paths without any human supervision, surpassing GPT-4o by over 20% in success rates.

comment: EMNLP 2025

♻ ★ Nav-SCOPE: Swarm Robot Cooperative Perception and Coordinated Navigation

Chenxi Li, Weining Lu, Qingquan Lin, Litong Meng, Haolu Li, Bin Liang

This paper proposes a lightweight systematic solution for multi-robot coordinated navigation with decentralized cooperative perception. An information flow is first created to facilitate real-time observation sharing over unreliable ad-hoc networks. Then, the environmental uncertainties of each robot are reduced by interaction fields that deliver complementary information. Finally, path optimization is achieved, enabling self-organized coordination with effective convergence, divergence, and collision avoidance. Our method is fully interpretable and ready for deployment without gaps. Comprehensive simulations and real-world experiments demonstrate reduced path redundancy, robust performance across various tasks, and minimal demands on computation and communication.

comment: 11 pages, 9 figures, accepted in IEEE Transactions on Automation Science and Engineering

♻ ★ EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for General Robot Control

Delin Qu, Haoming Song, Qizhi Chen, Zhaoqing Chen, Xianqiang Gao, Xinyi Ye, Qi Lv, Modi Shi, Guanghui Ren, Cheng Ruan, Maoqing Yao, Haoran Yang, Jiacheng Bao, Bin Zhao, Dong Wang

The human ability to seamlessly perform multimodal reasoning and physical interaction in the open world is a core goal for general-purpose embodied intelligent systems. Recent vision-language-action (VLA) models, which are co-trained on large-scale robot and visual-text data, have demonstrated notable progress in general robot control. However, they still fail to achieve human-level flexibility in interleaved reasoning and interaction. In this work, introduce EO-Robotics, consists of EO-1 model and EO-Data1.5M dataset. EO-1 is a unified embodied foundation model that achieves superior performance in multimodal embodied reasoning and robot control through interleaved vision-text-action pre-training. The development of EO-1 is based on two key pillars: (i) a unified architecture that processes multimodal inputs indiscriminately (image, text, video, and action), and (ii) a massive, high-quality multimodal embodied reasoning dataset, EO-Data1.5M, which contains over 1.5 million samples with emphasis on interleaved vision-text-action comprehension. EO-1 is trained through synergies between auto-regressive decoding and flow matching denoising on EO-Data1.5M, enabling seamless robot action generation and multimodal embodied reasoning. Extensive experiments demonstrate the effectiveness of interleaved vision-text-action learning for open-world understanding and generalization, validated through a variety of long-horizon, dexterous manipulation tasks across multiple embodiments. This paper details the architecture of EO-1, the data construction strategy of EO-Data1.5M, and the training methodology, offering valuable insights for developing advanced embodied foundation models.

♻ ★ The challenge of hidden gifts in multi-agent reinforcement learning

Dane Malenfant, Blake A. Richards

Sometimes we benefit from actions that others have taken even when we are unaware that they took those actions. For example, if your neighbor chooses not to take a parking spot in front of your house when you are not there, you can benefit, even without being aware that they took this action. These "hidden gifts" represent an interesting challenge for multi-agent reinforcement learning (MARL), since assigning credit when the beneficial actions of others are hidden is non-trivial. Here, we study the impact of hidden gifts with a very simple MARL task. In this task, agents in a grid-world environment have individual doors to unlock in order to obtain individual rewards. As well, if all the agents unlock their door the group receives a larger collective reward. However, there is only one key for all of the doors, such that the collective reward can only be obtained when the agents drop the key for others after they use it. Notably, there is nothing to indicate to an agent that the other agents have dropped the key, thus the act of dropping the key for others is a "hidden gift". We show that several different state-of-the-art RL algorithms, including MARL algorithms, fail to learn how to obtain the collective reward in this simple task. Interestingly, we find that independent model-free policy gradient agents can solve the task when we provide them with information about their own action history, but MARL agents still cannot solve the task with action history. Finally, we derive a correction term for these independent agents, inspired by learning aware approaches, which reduces the variance in learning and helps them to converge to collective success more reliably. These results show that credit assignment in multi-agent settings can be particularly challenging in the presence of "hidden gifts", and demonstrate that learning awareness in independent agents can benefit these settings.

comment: Updated proof section and included single agent baseline for key-to-door in the appendix. Related work now is part of the main section

♻ ★ RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms

Ziyao Wang, Rongpeng Li, Sizhao Li, Yuming Xiang, Haiping Wang, Zhifeng Zhao, Honggang Zhang

Intelligent control of Unmanned Aerial Vehicles (UAVs) swarms has emerged as a critical research focus, and it typically requires the swarm to navigate effectively while avoiding obstacles and achieving continuous coverage over multiple mission targets. Although traditional Multi-Agent Reinforcement Learning (MARL) approaches offer dynamic adaptability, they are hindered by the semantic gap in numerical communication and the rigidity of homogeneous role structures, resulting in poor generalization and limited task scalability. Recent advances in Large Language Model (LLM)-based control frameworks demonstrate strong semantic reasoning capabilities by leveraging extensive prior knowledge. However, due to the lack of online learning and over-reliance on static priors, these works often struggle with effective exploration, leading to reduced individual potential and overall system performance. To address these limitations, we propose a Role-Adaptive LLM-Driven Yoked navigation algorithm RALLY. Specifically, we first develop an LLM-driven semantic decision framework that uses structured natural language for efficient semantic communication and collaborative reasoning. Afterward, we introduce a dynamic role-heterogeneity mechanism for adaptive role switching and personalized decision-making. Furthermore, we propose a Role-value Mixing Network (RMIX)-based assignment strategy that integrates LLM offline priors with MARL online policies to enable semi-offline training of role selection strategies. Experiments in the Multi-Agent Particle Environment (MPE) environment and a Software-In-The-Loop (SITL) platform demonstrate that RALLY outperforms conventional approaches in terms of task coverage, convergence speed, and generalization, highlighting its strong potential for collaborative navigation in agentic multi-UAV systems.

♻ ★ Distributed Fractional Bayesian Learning for Adaptive Optimization

Yaqun Yang, Jinlong Lei, Guanghui Wen, Yiguang Hong

This paper considers a distributed adaptive optimization problem, where all agents only have access to their local cost functions with a common unknown parameter, whereas they mean to collaboratively estimate the true parameter and find the optimal solution over a connected network. A general mathematical framework for such a problem has not been studied yet. We aim to provide valuable insights for addressing parameter uncertainty in distributed optimization problems and simultaneously find the optimal solution. Thus, we propose a novel distributed scheme, which utilizes distributed fractional Bayesian learning through weighted averaging on the log-beliefs to update the beliefs of unknown parameter, and distributed gradient descent for renewing the estimation of the optimal solution. Then under suitable assumptions, we prove that all agents' beliefs and decision variables converge almost surely to the true parameter and the optimal solution under the true parameter, respectively. We further establish a sublinear convergence rate for the belief sequence. Finally, numerical experiments are implemented to corroborate the theoretical analysis.

♻ ★ ORCA: ORchestrating Causal Agent

Joanie Hayoun Chung, Chaemyung Lim, Sumin Lee, Songseong Kim, Sungbin Lim

Causal inference is essential for decision-making science while the complexity of the data analysis workflow, ranging from data wrangling to causal analysis, increases substantially as the scale of data grows in complicated business environments. Especially, the execution of the workflow in relational databases by non-experts can result in repetitive bottlenecks which impede timely and responsible business insights. To address this challenge, we propose ORCA (Orchestrating Causal Agent), an LLM agentic system that can automate routine workflows in RDBMS while preserving expert oversight via human-AI interactions. ORCA orchestrates the full data analysis pipeline: interpreting natural language queries, navigating tables from DB servers, generating proper SQL codes, preprocessing data, and configuring modeling processes using causal inference libraries. Domain experts still can control the automation through iterative interactions with ORCA, enabling robust data-driven decision making with less technical expertise in statistical computing. Empirical evaluations on benchmark and synthetic e-commerce datasets demonstrate competitive performance of ORCA in table understanding, query generation, and cause-effect estimation -- achieving over $7\times$ improvement in estimating average treatment compared to GPT-4o mini.

comment: 24 pages, 17 figures, 1 table

♻ ★ Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids

Toru Lin, Kartik Sachdev, Linxi Fan, Jitendra Malik, Yuke Zhu

Learning generalizable robot manipulation policies, especially for complex multi-fingered humanoids, remains a significant challenge. Existing approaches primarily rely on extensive data collection and imitation learning, which are expensive, labor-intensive, and difficult to scale. Sim-to-real reinforcement learning (RL) offers a promising alternative, but has mostly succeeded in simpler state-based or single-hand setups. How to effectively extend this to vision-based, contact-rich bimanual manipulation tasks remains an open question. In this paper, we introduce a practical sim-to-real RL recipe that trains a humanoid robot to perform three challenging dexterous manipulation tasks: grasp-and-reach, box lift and bimanual handover. Our method features an automated real-to-sim tuning module, a generalized reward formulation based on contact and object goals, a divide-and-conquer policy distillation framework, and a hybrid object representation strategy with modality-specific augmentation. We demonstrate high success rates on unseen objects and robust, adaptive policy behaviors -- highlighting that vision-based dexterous manipulation via sim-to-real RL is not only viable, but also scalable and broadly applicable to real-world humanoid manipulation tasks.

comment: Published at CoRL 2025. Project page can be found at https://toruowo.github.io/recipe/

♻ ★ Directional excitability in Hilbert spaces

Gustave Bainier, Alessio Franci

We introduce a generalized excitable system in which spikes can happen in a continuum of directions, therefore drastically enriching the expressivity and control capability of the spiking dynamics. In this generalized excitable system, spiking trajectories happen in a Hilbert space with an excitable resting state at the origin and spike responses that can be triggered in any direction as a function of the system's state and inputs. State-dependence of the spiking direction provide the system with a vanishing spiking memory trace, which enables robust tracking and integration of inputs in the spiking direction history. The model exhibits generalized forms of both Hodgkin's Type I and Type II excitability, capturing their usual bifurcation behaviors in an abstract setting. When used as the controller of a two-dimensional navigation task, this model facilitates both the sparseness of the actuation and its sensitivity to environmental inputs. These results highlight the potential of the proposed generalized excitable model for excitable control in high- and infinite-dimensional spaces.

comment: 6 pages, 7 figures

♻ ★ Decentralized Parametric Stability Certificates for Grid-Forming Converter Control

Verena Häberle, Xiuqiang He, Linbin Huang, Florian Dörfler, Steven Low

We propose a decentralized framework for guaranteeing the small-signal stability of future power systems with grid-forming converters. Our approach leverages dynamic loop-shifting techniques to compensate for the lack of passivity in the network dynamics and establishes decentralized parametric stability certificates, depending on the local device-level controls and incorporating the effects of the network dynamics. By following practical tuning rules, we are able to ensure plug-and-play operation without centralized coordination. Unlike prior works, our approach accommodates coupled frequency and voltage dynamics, incorporates network dynamics, and does not rely on specific network configurations or operating points, offering a general and scalable solution for the integration of power-electronics-based devices into future power systems. We validate our theoretical stability results through numerical case studies in a high-fidelity simulation model.

comment: 13 pages, 15 figures

♻ ★ Composable Uncertainty in Symmetric Monoidal Categories for Design Problems (Extended Version)

Marius Furter, Yujun Huang, Gioele Zardini

Applied category theory often studies symmetric monoidal categories (SMCs) whose morphisms represent open systems. These structures naturally accommodate complex wiring patterns, leveraging (co)monoidal structures for splitting and merging wires, or compact closed structures for feedback. A key example is the compact closed SMC of design problems (DP), which enables a compositional approach to co-design in engineering. However, in practice, the systems of interest may not be fully known. Recently, Markov categories have emerged as a powerful framework for modeling uncertain processes. In this work, we demonstrate how to integrate this perspective into the study of open systems while preserving consistency with the underlying SMC structure. To this end, we employ the change-of-base construction for enriched categories, replacing the morphisms of a symmetric monoidal $\mathcal{V}$-category $\mathcal{C}$ with parametric maps $A \to \mathcal{C}(X,Y)$ in a Markov category induced by a symmetric monoidal monad. This results in a symmetric monoidal 2-category $N_*\mathcal{C}$ with the same objects as $\mathcal{C}$ and reparametrization 2-cells. By choosing different monads, we capture various types of uncertainty. The category underlying $\mathcal{C}$ embeds into $N_*\mathcal{C}$ via a strict symmetric monoidal functor, allowing (co)monoidal and compact closed structures to be transferred. Applied to DP, this construction leads to categories of practical relevance, such as parametrized design problems for optimization, and parametrized distributions of design problems for decision theory and Bayesian learning.

comment: 23 pages, 2 figures, accepted to Applied Category Theory 2025

♻ ★ Current trends and future directions in event-based control

Michael Hertneck, David Meister, Frank Allgöwer

The defining characteristic of event-based control is that feedback loops are only closed when indicated by a triggering condition that takes recent information about the system into account. This stands in contrast to periodic control where the feedback loop is closed periodically. Benefits of event-based control arise when sampling comes at a cost, which occurs, e.g., for Networked Control Systems or in other setups with resource constraints. A rapidly growing number of publications deals with event-based control. Nevertheless, some fundamental questions about event-based control are still unsolved. In this article, we provide an overview of current research trends in event-based control. We focus on results that aim for a better understanding of effects that occur in feedback loops with event-based control. Based on this summary, we identify important open directions for future research.

comment: Submitted to the European Journal of Control

♻ ★ Adaptive control of dynamic networks

Chunyu Pan, Xizhe Zhang, Haoyu Zheng, Zhao Su, Changsheng Zhang, Weixiong Zhang

Real-world network systems are inherently dynamic, with network topologies undergoing continuous changes over time. Previous works often focus on static networks or rely on complete prior knowledge of evolving topologies, whereas real-world networks typically undergo stochastic structural changes that are difficult to predict in advance. To address this challenge, we define the adaptive control problem and propose an adaptive control algorithm to reduce the extra control cost caused by driver node switching. We introduce a node-level adaptive control metric to capture both the stability and consistency of each node across historical topologies. By integrating this metric with a partial matching repair strategy, our algorithm adjusts the minimum driver node set in real time at each snapshot, while minimizing unnecessary reconfigurations between consecutive time steps. Extensive experiments on synthetic and real-world dynamic networks demonstrate that the proposed adaptive control algorithm significantly outperforms the existing algorithm, reducing the switching cost by an average of 22% in synthetic networks and 19\% in real-world networks, without requiring foreknowledge of the future evolution of the network. These findings extend the theoretical scope of dynamic network controllability and open new avenues for practical applications in transportation, social, and molecular regulatory systems.

♻ ★ Beyond Asymptotics: Targeted exploration with finite-sample guarantees

Janani Venkatasubramanian, Johannes Köhler, Frank Allgöwer

In this paper, we introduce a targeted exploration strategy for the non-asymptotic, finite-time case. The proposed strategy is applicable to uncertain linear time-invariant systems subject to sub-Gaussian disturbances. As the main result, the proposed approach provides a priori guarantees, ensuring that the optimized exploration inputs achieve a desired accuracy of the model parameters. The technical derivation of the strategy (i) leverages existing non-asymptotic identification bounds with self-normalized martingales, (ii) utilizes spectral lines to predict the effect of sinusoidal excitation, and (iii) effectively accounts for spectral transient error and parametric uncertainty. A numerical example illustrates how the finite exploration time influence the required exploration energy.

comment: Extended paper with proofs, CDC 2025

♻ ★ Feedback Optimization with State Constraints through Control Barrier Functions

Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés, W. P. M. H. Heemels

Recently, there has been a surge of research on a class of methods called feedback optimization. These are methods to steer the state of a control system to an equilibrium that arises as the solution of an optimization problem. Despite the growing literature on the topic, the important problem of enforcing state constraints at all times remains unaddressed. In this work, we present the first feedback-optimization method that enforces state constraints. The method combines a class of dynamics called safe gradient flows with high-order control barrier functions. We provide a number of results on our proposed controller, including well-posedness guarantees, anytime constraint-satisfaction guarantees, equivalence between the closed-loop's equilibria and the optimization problem's critical points, and local asymptotic stability of optima.

comment: accepted at the 64th IEEE Conference on Decision and Control (CDC), 2025

♻ ★ Robust MPC for Uncertain Linear Systems - Combining Model Adaptation and Iterative Learning

Hannes Petrenz, Johannes Köhler, Francesco Borrelli

This paper presents a robust adaptive learning Model Predictive Control (MPC) framework for linear systems with parametric uncertainties and additive disturbances performing iterative tasks. The approach refines the parameter estimates online using set-membership estimation. Performance enhancement over iterations is achieved by learning the terminal cost from data. Safety is enforced using a terminal set, which is also learned iteratively. The proposed method guarantees recursive feasibility, constraint satisfaction, and a robust bound on the closed-loop cost. Numerical simulations on a mass-spring-damper system demonstrate improved computational efficiency and control performance compared to a robust adaptive MPC scheme without iterative learning of the terminal ingredients.

comment: Github link to the example: https://github.com/HannesPetrenz/RALMPC_Linear_Uncertain_Systems

♻ ★ Combined Stochastic and Robust Optimization for Electric Autonomous Mobility-on-Demand with Nested Benders Decomposition

Sten Elling Tingstad Jacobsen, Balázs Kulcsár, Anders Lindman

The electrification and automation of mobility are reshaping how cities operate on-demand transport systems. Managing Electric Autonomous Mobility-on-Demand (EAMoD) fleets effectively requires coordinating dispatch, rebalancing, and charging decisions under multiple uncertainties, including travel demand, travel time, energy consumption, and charger availability. We address this challenge with a combined stochastic and robust model predictive control (MPC) framework. The framework integrates spatio-temporal Bayesian neural network forecasts with a multi-stage stochastic optimization model, formulated as a large-scale mixed-integer linear program. To ensure real-time applicability, we develop a tailored Nested Benders Decomposition that exploits the scenario tree structure and enables efficient parallelized solution. Stochastic optimization is employed to anticipate demand and infrastructure variability, while robust constraints on energy consumption and travel times safeguard feasibility under worst-case realizations. We evaluate the framework using high-fidelity simulations of San Francisco and Chicago. Compared with deterministic, reactive, and robust baselines, the combined stochastic and robust approach reduces median passenger waiting times by up to 36% and 95th-percentile delays by nearly 20%, while also lowering rebalancing distance by 27% and electricity costs by more than 35%. We also conduct a sensitivity analysis of battery size and vehicle efficiency, finding that energy-efficient vehicles maintain stable performance even with small batteries, whereas less efficient vehicles require larger batteries and greater infrastructure support. Our results emphasize the importance of jointly optimizing predictive control, vehicle capabilities, and infrastructure planning to enable scalable, cost-efficient EAMoD operations.

comment: 29 pages, 12 figures

♻ ★ Consensus in Multiagent Systems under communication failure

Mohamed Bentaibi, Laura Caravenna, Jean-Paul A. Gauthier, Francesco Rossi

We consider multi-agent systems with cooperative interactions and study the convergence to consensus in the case of time-dependent connections, with possible communication failure. We prove a new condition ensuring consensus: we define a graph in which directed arrows correspond to connection functions that converge (in the weak sense) to some function with a positive integral on all intervals of the form $[t,+\infty)$. If the graph has a node reachable from all other indices, i.e.~``globally reachable'', then the system converges to consensus. We show that this requirement generalizes some known sufficient conditions for convergence, such as Moreau's or the Persistent Excitation one. We also give a second new condition, transversal to the known ones: total connectedness of the undirected graph formed by the non-vanishing of limiting functions.

♻ ★ A First-Order Gradient Approach for the Connectivity Optimization of Markov Chains

Christian P. C. Franssen, Alessandro Zocca, Bernd F. Heidergott

Graphs are commonly used to model various complex systems, including social networks, power grids, transportation networks, and biological systems. In many applications, the connectivity of these networks can be expressed through the Mean First Passage Times (MFPTs) of a Markov chain modeling a random walker on the graph. In this paper, we generalize the network metrics based on Markov chains' MFPTs and extend them to networks affected by uncertainty, in which edges may fail and hence not be present according to a pre-determined stochastic model. To find optimally connected Markov chains, we present a parameterization-free method for optimizing the MFPTs of the Markov chain. More specifically, we present an efficient Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm in the context of Markov chain optimization. The proposed algorithm is suitable for both fixed and random networks. Using various numerical experiments, we demonstrate scalability compared to established benchmarks. Importantly, our algorithm finds an optimal solution without requiring prior knowledge of edge failure probabilities, allowing for an online optimization approach.

♻ ★ CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement

Lei Cheng, Lihao Guo, Tianya Zhang, Tam Bang, Austin Harris, Mustafa Hajij, Mina Sartipi, Siyang Cao

Accurate multi-sensor calibration is essential for deploying robust perception systems in applications such as autonomous driving and intelligent transportation. Existing LiDAR-camera calibration methods often rely on manually placed targets, preliminary parameter estimates, or intensive data preprocessing, limiting their scalability and adaptability in real-world settings. In this work, we propose a fully automatic, targetless, and online calibration framework, CalibRefine, which directly processes raw LiDAR point clouds and camera images. Our approach is divided into four stages: (1) a Common Feature Discriminator that leverages relative spatial positions, visual appearance embeddings, and semantic class cues to identify and generate reliable LiDAR-camera correspondences, (2) a coarse homography-based calibration that uses the matched feature correspondences to estimate an initial transformation between the LiDAR and camera frames, serving as the foundation for further refinement, (3) an iterative refinement to incrementally improve alignment as additional data frames become available, and (4) an attention-based refinement that addresses non-planar distortions by leveraging a Vision Transformer and cross-attention mechanisms. Extensive experiments on two urban traffic datasets demonstrate that CalibRefine achieves high-precision calibration with minimal human input, outperforming state-of-the-art targetless methods and matching or surpassing manually tuned baselines. Our results show that robust object-level feature matching, combined with iterative refinement and self-supervised attention-based refinement, enables reliable sensor alignment in complex real-world conditions without ground-truth matrices or elaborate preprocessing. Code is available at https://github.com/radar-lab/Lidar_Camera_Automatic_Calibration

♻ ★ Offset-free model predictive control: stability under plant-model mismatch

Steven J. Kuntz, James B. Rawlings

We present the first general stability results for nonlinear offset-free model predictive control (MPC). Despite over twenty years of active research, the offset-free MPC literature has not shaken the assumption of closed-loop stability for establishing offset-free performance. In this paper, we present a nonlinear offset-free MPC design that is robustly stable with respect to the tracking errors, and thus achieves offset-free performance, despite plant-model mismatch and persistent disturbances. Key features and assumptions of this design include quadratic costs, differentiability of the plant and model functions, constraint backoffs at steady state, and a robustly stable state and disturbance estimator. We first establish nominal stability and offset-free performance. Then, robustness to state and disturbance estimate errors and setpoint and disturbance changes is demonstrated. Finally, the results are extended to sufficiently small plant-model mismatch. The results are illustrated by numerical examples.

comment: 56 pages, 4 figures

♻ ★ Best Response Convergence for Zero-sum Stochastic Dynamic Games with Partial and Asymmetric Information

Yuxiang Guan, Iman Shames, Tyler H. Summers

We analyze best response dynamics for finding a Nash equilibrium of an infinite horizon zero-sum stochastic linear quadratic dynamic game (LQDG) with partial and asymmetric information. We derive explicit expressions for each player's best response within the class of pure linear dynamic output feedback control strategies where the internal state dimension of each control strategy is an integer multiple of the system state dimension. With each best response, the players form increasingly higher-order belief states, leading to infinite-dimensional internal states. However, we observe in extensive numerical experiments that the game's value converges after just a few iterations, suggesting that strategies associated with increasingly higher-order belief states eventually provide no benefit. To help explain this convergence, our numerical analysis reveals rapid decay of the controllability and observability Gramian eigenvalues and Hankel singular values in higher-order belief dynamics, indicating that the higher-order belief dynamics become increasingly difficult for both players to control and observe. Consequently, the higher-order belief dynamics can be closely approximated by low-order belief dynamics with bounded error, and thus feedback strategies with limited internal state dimension can closely approximate a Nash equilibrium.

♻ ★ Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids

Toru Lin, Kartik Sachdev, Linxi Fan, Jitendra Malik, Yuke Zhu

Learning generalizable robot manipulation policies, especially for complex multi-fingered humanoids, remains a significant challenge. Existing approaches primarily rely on extensive data collection and imitation learning, which are expensive, labor-intensive, and difficult to scale. Sim-to-real reinforcement learning (RL) offers a promising alternative, but has mostly succeeded in simpler state-based or single-hand setups. How to effectively extend this to vision-based, contact-rich bimanual manipulation tasks remains an open question. In this paper, we introduce a practical sim-to-real RL recipe that trains a humanoid robot to perform three challenging dexterous manipulation tasks: grasp-and-reach, box lift and bimanual handover. Our method features an automated real-to-sim tuning module, a generalized reward formulation based on contact and object goals, a divide-and-conquer policy distillation framework, and a hybrid object representation strategy with modality-specific augmentation. We demonstrate high success rates on unseen objects and robust, adaptive policy behaviors -- highlighting that vision-based dexterous manipulation via sim-to-real RL is not only viable, but also scalable and broadly applicable to real-world humanoid manipulation tasks.

comment: Published at CoRL 2025. Project page can be found at https://toruowo.github.io/recipe/

♻ ★ Directional excitability in Hilbert spaces

Gustave Bainier, Alessio Franci

We introduce a generalized excitable system in which spikes can happen in a continuum of directions, therefore drastically enriching the expressivity and control capability of the spiking dynamics. In this generalized excitable system, spiking trajectories happen in a Hilbert space with an excitable resting state at the origin and spike responses that can be triggered in any direction as a function of the system's state and inputs. State-dependence of the spiking direction provide the system with a vanishing spiking memory trace, which enables robust tracking and integration of inputs in the spiking direction history. The model exhibits generalized forms of both Hodgkin's Type I and Type II excitability, capturing their usual bifurcation behaviors in an abstract setting. When used as the controller of a two-dimensional navigation task, this model facilitates both the sparseness of the actuation and its sensitivity to environmental inputs. These results highlight the potential of the proposed generalized excitable model for excitable control in high- and infinite-dimensional spaces.

comment: 6 pages, 7 figures

♻ ★ Decentralized Parametric Stability Certificates for Grid-Forming Converter Control

Verena Häberle, Xiuqiang He, Linbin Huang, Florian Dörfler, Steven Low

We propose a decentralized framework for guaranteeing the small-signal stability of future power systems with grid-forming converters. Our approach leverages dynamic loop-shifting techniques to compensate for the lack of passivity in the network dynamics and establishes decentralized parametric stability certificates, depending on the local device-level controls and incorporating the effects of the network dynamics. By following practical tuning rules, we are able to ensure plug-and-play operation without centralized coordination. Unlike prior works, our approach accommodates coupled frequency and voltage dynamics, incorporates network dynamics, and does not rely on specific network configurations or operating points, offering a general and scalable solution for the integration of power-electronics-based devices into future power systems. We validate our theoretical stability results through numerical case studies in a high-fidelity simulation model.

comment: 13 pages, 15 figures

♻ ★ Composable Uncertainty in Symmetric Monoidal Categories for Design Problems (Extended Version)

Marius Furter, Yujun Huang, Gioele Zardini

Applied category theory often studies symmetric monoidal categories (SMCs) whose morphisms represent open systems. These structures naturally accommodate complex wiring patterns, leveraging (co)monoidal structures for splitting and merging wires, or compact closed structures for feedback. A key example is the compact closed SMC of design problems (DP), which enables a compositional approach to co-design in engineering. However, in practice, the systems of interest may not be fully known. Recently, Markov categories have emerged as a powerful framework for modeling uncertain processes. In this work, we demonstrate how to integrate this perspective into the study of open systems while preserving consistency with the underlying SMC structure. To this end, we employ the change-of-base construction for enriched categories, replacing the morphisms of a symmetric monoidal $\mathcal{V}$-category $\mathcal{C}$ with parametric maps $A \to \mathcal{C}(X,Y)$ in a Markov category induced by a symmetric monoidal monad. This results in a symmetric monoidal 2-category $N_*\mathcal{C}$ with the same objects as $\mathcal{C}$ and reparametrization 2-cells. By choosing different monads, we capture various types of uncertainty. The category underlying $\mathcal{C}$ embeds into $N_*\mathcal{C}$ via a strict symmetric monoidal functor, allowing (co)monoidal and compact closed structures to be transferred. Applied to DP, this construction leads to categories of practical relevance, such as parametrized design problems for optimization, and parametrized distributions of design problems for decision theory and Bayesian learning.

comment: 23 pages, 2 figures, accepted to Applied Category Theory 2025

♻ ★ Current trends and future directions in event-based control

Michael Hertneck, David Meister, Frank Allgöwer

The defining characteristic of event-based control is that feedback loops are only closed when indicated by a triggering condition that takes recent information about the system into account. This stands in contrast to periodic control where the feedback loop is closed periodically. Benefits of event-based control arise when sampling comes at a cost, which occurs, e.g., for Networked Control Systems or in other setups with resource constraints. A rapidly growing number of publications deals with event-based control. Nevertheless, some fundamental questions about event-based control are still unsolved. In this article, we provide an overview of current research trends in event-based control. We focus on results that aim for a better understanding of effects that occur in feedback loops with event-based control. Based on this summary, we identify important open directions for future research.

comment: Submitted to the European Journal of Control

♻ ★ Adaptive control of dynamic networks

Chunyu Pan, Xizhe Zhang, Haoyu Zheng, Zhao Su, Changsheng Zhang, Weixiong Zhang

Real-world network systems are inherently dynamic, with network topologies undergoing continuous changes over time. Previous works often focus on static networks or rely on complete prior knowledge of evolving topologies, whereas real-world networks typically undergo stochastic structural changes that are difficult to predict in advance. To address this challenge, we define the adaptive control problem and propose an adaptive control algorithm to reduce the extra control cost caused by driver node switching. We introduce a node-level adaptive control metric to capture both the stability and consistency of each node across historical topologies. By integrating this metric with a partial matching repair strategy, our algorithm adjusts the minimum driver node set in real time at each snapshot, while minimizing unnecessary reconfigurations between consecutive time steps. Extensive experiments on synthetic and real-world dynamic networks demonstrate that the proposed adaptive control algorithm significantly outperforms the existing algorithm, reducing the switching cost by an average of 22% in synthetic networks and 19\% in real-world networks, without requiring foreknowledge of the future evolution of the network. These findings extend the theoretical scope of dynamic network controllability and open new avenues for practical applications in transportation, social, and molecular regulatory systems.

♻ ★ Beyond Asymptotics: Targeted exploration with finite-sample guarantees

Janani Venkatasubramanian, Johannes Köhler, Frank Allgöwer

In this paper, we introduce a targeted exploration strategy for the non-asymptotic, finite-time case. The proposed strategy is applicable to uncertain linear time-invariant systems subject to sub-Gaussian disturbances. As the main result, the proposed approach provides a priori guarantees, ensuring that the optimized exploration inputs achieve a desired accuracy of the model parameters. The technical derivation of the strategy (i) leverages existing non-asymptotic identification bounds with self-normalized martingales, (ii) utilizes spectral lines to predict the effect of sinusoidal excitation, and (iii) effectively accounts for spectral transient error and parametric uncertainty. A numerical example illustrates how the finite exploration time influence the required exploration energy.

comment: Extended paper with proofs, CDC 2025

♻ ★ Feedback Optimization with State Constraints through Control Barrier Functions

Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés, W. P. M. H. Heemels

Recently, there has been a surge of research on a class of methods called feedback optimization. These are methods to steer the state of a control system to an equilibrium that arises as the solution of an optimization problem. Despite the growing literature on the topic, the important problem of enforcing state constraints at all times remains unaddressed. In this work, we present the first feedback-optimization method that enforces state constraints. The method combines a class of dynamics called safe gradient flows with high-order control barrier functions. We provide a number of results on our proposed controller, including well-posedness guarantees, anytime constraint-satisfaction guarantees, equivalence between the closed-loop's equilibria and the optimization problem's critical points, and local asymptotic stability of optima.

comment: accepted at the 64th IEEE Conference on Decision and Control (CDC), 2025

♻ ★ Robust MPC for Uncertain Linear Systems - Combining Model Adaptation and Iterative Learning

Hannes Petrenz, Johannes Köhler, Francesco Borrelli

This paper presents a robust adaptive learning Model Predictive Control (MPC) framework for linear systems with parametric uncertainties and additive disturbances performing iterative tasks. The approach refines the parameter estimates online using set-membership estimation. Performance enhancement over iterations is achieved by learning the terminal cost from data. Safety is enforced using a terminal set, which is also learned iteratively. The proposed method guarantees recursive feasibility, constraint satisfaction, and a robust bound on the closed-loop cost. Numerical simulations on a mass-spring-damper system demonstrate improved computational efficiency and control performance compared to a robust adaptive MPC scheme without iterative learning of the terminal ingredients.

comment: Github link to the example: https://github.com/HannesPetrenz/RALMPC_Linear_Uncertain_Systems

♻ ★ Combined Stochastic and Robust Optimization for Electric Autonomous Mobility-on-Demand with Nested Benders Decomposition

Sten Elling Tingstad Jacobsen, Balázs Kulcsár, Anders Lindman

The electrification and automation of mobility are reshaping how cities operate on-demand transport systems. Managing Electric Autonomous Mobility-on-Demand (EAMoD) fleets effectively requires coordinating dispatch, rebalancing, and charging decisions under multiple uncertainties, including travel demand, travel time, energy consumption, and charger availability. We address this challenge with a combined stochastic and robust model predictive control (MPC) framework. The framework integrates spatio-temporal Bayesian neural network forecasts with a multi-stage stochastic optimization model, formulated as a large-scale mixed-integer linear program. To ensure real-time applicability, we develop a tailored Nested Benders Decomposition that exploits the scenario tree structure and enables efficient parallelized solution. Stochastic optimization is employed to anticipate demand and infrastructure variability, while robust constraints on energy consumption and travel times safeguard feasibility under worst-case realizations. We evaluate the framework using high-fidelity simulations of San Francisco and Chicago. Compared with deterministic, reactive, and robust baselines, the combined stochastic and robust approach reduces median passenger waiting times by up to 36% and 95th-percentile delays by nearly 20%, while also lowering rebalancing distance by 27% and electricity costs by more than 35%. We also conduct a sensitivity analysis of battery size and vehicle efficiency, finding that energy-efficient vehicles maintain stable performance even with small batteries, whereas less efficient vehicles require larger batteries and greater infrastructure support. Our results emphasize the importance of jointly optimizing predictive control, vehicle capabilities, and infrastructure planning to enable scalable, cost-efficient EAMoD operations.

comment: 29 pages, 12 figures

♻ ★ Consensus in Multiagent Systems under communication failure

Mohamed Bentaibi, Laura Caravenna, Jean-Paul A. Gauthier, Francesco Rossi

We consider multi-agent systems with cooperative interactions and study the convergence to consensus in the case of time-dependent connections, with possible communication failure. We prove a new condition ensuring consensus: we define a graph in which directed arrows correspond to connection functions that converge (in the weak sense) to some function with a positive integral on all intervals of the form $[t,+\infty)$. If the graph has a node reachable from all other indices, i.e.~``globally reachable'', then the system converges to consensus. We show that this requirement generalizes some known sufficient conditions for convergence, such as Moreau's or the Persistent Excitation one. We also give a second new condition, transversal to the known ones: total connectedness of the undirected graph formed by the non-vanishing of limiting functions.

♻ ★ A First-Order Gradient Approach for the Connectivity Optimization of Markov Chains

Christian P. C. Franssen, Alessandro Zocca, Bernd F. Heidergott

Graphs are commonly used to model various complex systems, including social networks, power grids, transportation networks, and biological systems. In many applications, the connectivity of these networks can be expressed through the Mean First Passage Times (MFPTs) of a Markov chain modeling a random walker on the graph. In this paper, we generalize the network metrics based on Markov chains' MFPTs and extend them to networks affected by uncertainty, in which edges may fail and hence not be present according to a pre-determined stochastic model. To find optimally connected Markov chains, we present a parameterization-free method for optimizing the MFPTs of the Markov chain. More specifically, we present an efficient Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm in the context of Markov chain optimization. The proposed algorithm is suitable for both fixed and random networks. Using various numerical experiments, we demonstrate scalability compared to established benchmarks. Importantly, our algorithm finds an optimal solution without requiring prior knowledge of edge failure probabilities, allowing for an online optimization approach.

♻ ★ CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement

Lei Cheng, Lihao Guo, Tianya Zhang, Tam Bang, Austin Harris, Mustafa Hajij, Mina Sartipi, Siyang Cao

Accurate multi-sensor calibration is essential for deploying robust perception systems in applications such as autonomous driving and intelligent transportation. Existing LiDAR-camera calibration methods often rely on manually placed targets, preliminary parameter estimates, or intensive data preprocessing, limiting their scalability and adaptability in real-world settings. In this work, we propose a fully automatic, targetless, and online calibration framework, CalibRefine, which directly processes raw LiDAR point clouds and camera images. Our approach is divided into four stages: (1) a Common Feature Discriminator that leverages relative spatial positions, visual appearance embeddings, and semantic class cues to identify and generate reliable LiDAR-camera correspondences, (2) a coarse homography-based calibration that uses the matched feature correspondences to estimate an initial transformation between the LiDAR and camera frames, serving as the foundation for further refinement, (3) an iterative refinement to incrementally improve alignment as additional data frames become available, and (4) an attention-based refinement that addresses non-planar distortions by leveraging a Vision Transformer and cross-attention mechanisms. Extensive experiments on two urban traffic datasets demonstrate that CalibRefine achieves high-precision calibration with minimal human input, outperforming state-of-the-art targetless methods and matching or surpassing manually tuned baselines. Our results show that robust object-level feature matching, combined with iterative refinement and self-supervised attention-based refinement, enables reliable sensor alignment in complex real-world conditions without ground-truth matrices or elaborate preprocessing. Code is available at https://github.com/radar-lab/Lidar_Camera_Automatic_Calibration

♻ ★ Offset-free model predictive control: stability under plant-model mismatch

Steven J. Kuntz, James B. Rawlings

We present the first general stability results for nonlinear offset-free model predictive control (MPC). Despite over twenty years of active research, the offset-free MPC literature has not shaken the assumption of closed-loop stability for establishing offset-free performance. In this paper, we present a nonlinear offset-free MPC design that is robustly stable with respect to the tracking errors, and thus achieves offset-free performance, despite plant-model mismatch and persistent disturbances. Key features and assumptions of this design include quadratic costs, differentiability of the plant and model functions, constraint backoffs at steady state, and a robustly stable state and disturbance estimator. We first establish nominal stability and offset-free performance. Then, robustness to state and disturbance estimate errors and setpoint and disturbance changes is demonstrated. Finally, the results are extended to sufficiently small plant-model mismatch. The results are illustrated by numerical examples.

comment: 56 pages, 4 figures

♻ ★ Best Response Convergence for Zero-sum Stochastic Dynamic Games with Partial and Asymmetric Information

Yuxiang Guan, Iman Shames, Tyler H. Summers

We analyze best response dynamics for finding a Nash equilibrium of an infinite horizon zero-sum stochastic linear quadratic dynamic game (LQDG) with partial and asymmetric information. We derive explicit expressions for each player's best response within the class of pure linear dynamic output feedback control strategies where the internal state dimension of each control strategy is an integer multiple of the system state dimension. With each best response, the players form increasingly higher-order belief states, leading to infinite-dimensional internal states. However, we observe in extensive numerical experiments that the game's value converges after just a few iterations, suggesting that strategies associated with increasingly higher-order belief states eventually provide no benefit. To help explain this convergence, our numerical analysis reveals rapid decay of the controllability and observability Gramian eigenvalues and Hankel singular values in higher-order belief dynamics, indicating that the higher-order belief dynamics become increasingly difficult for both players to control and observe. Consequently, the higher-order belief dynamics can be closely approximated by low-order belief dynamics with bounded error, and thus feedback strategies with limited internal state dimension can closely approximate a Nash equilibrium.

♻ ★ Unscented Kalman Filter with a Nonlinear Propagation Model for Navigation Applications

Amit Levy, Itzik Klein

The unscented Kalman filter is a nonlinear estimation algorithm commonly used in navigation applications. The prediction of the mean and covariance matrix is crucial to the stable behavior of the filter. This prediction is done by propagating the sigma points according to the dynamic model at hand. In this paper, we introduce an innovative method to propagate the sigma points according to the nonlinear dynamic model of the navigation error state vector. This improves the filter accuracy and navigation performance. We demonstrate the benefits of our proposed approach using real sensor data recorded by an autonomous underwater vehicle during several scenarios.

comment: 6 pages, 4 figures

♻ ★ HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation

Zhecheng Yuan, Tianming Wei, Langzhe Gu, Pu Hua, Tianhai Liang, Yuanpei Chen, Huazhe Xu

Leveraging human motion data to impart robots with versatile manipulation skills has emerged as a promising paradigm in robotic manipulation. Nevertheless, translating multi-source human hand motions into feasible robot behaviors remains challenging, particularly for robots equipped with multi-fingered dexterous hands characterized by complex, high-dimensional action spaces. Moreover, existing approaches often struggle to produce policies capable of adapting to diverse environmental conditions. In this paper, we introduce HERMES, a human-to-robot learning framework for mobile bimanual dexterous manipulation. First, HERMES formulates a unified reinforcement learning approach capable of seamlessly transforming heterogeneous human hand motions from multiple sources into physically plausible robotic behaviors. Subsequently, to mitigate the sim2real gap, we devise an end-to-end, depth image-based sim2real transfer method for improved generalization to real-world scenarios. Furthermore, to enable autonomous operation in varied and unstructured environments, we augment the navigation foundation model with a closed-loop Perspective-n-Point (PnP) localization mechanism, ensuring precise alignment of visual goals and effectively bridging autonomous navigation and dexterous manipulation. Extensive experimental results demonstrate that HERMES consistently exhibits generalizable behaviors across diverse, in-the-wild scenarios, successfully performing numerous complex mobile bimanual dexterous manipulation tasks. Project Page:https://gemcollector.github.io/HERMES/.

♻ ★ Making Physical Objects with Generative AI and Robotic Assembly: Considering Fabrication Constraints, Sustainability, Time, Functionality, and Accessibility

Alexander Htet Kyaw, Se Hwan Jeon, Miana Smith, Neil Gershenfeld

3D generative AI enables rapid and accessible creation of 3D models from text or image inputs. However, translating these outputs into physical objects remains a challenge due to the constraints in the physical world. Recent studies have focused on improving the capabilities of 3D generative AI to produce fabricable outputs, with 3D printing as the main fabrication method. However, this workshop paper calls for a broader perspective by considering how fabrication methods align with the capabilities of 3D generative AI. As a case study, we present a novel system using discrete robotic assembly and 3D generative AI to make physical objects. Through this work, we identified five key aspects to consider in a physical making process based on the capabilities of 3D generative AI. 1) Fabrication Constraints: Current text-to-3D models can generate a wide range of 3D designs, requiring fabrication methods that can adapt to the variability of generative AI outputs. 2) Time: While generative AI can generate 3D models in seconds, fabricating physical objects can take hours or even days. Faster production could enable a closer iterative design loop between humans and AI in the making process. 3) Sustainability: Although text-to-3D models can generate thousands of models in the digital world, extending this capability to the real world would be resource-intensive, unsustainable and irresponsible. 4) Functionality: Unlike digital outputs from 3D generative AI models, the fabrication method plays a crucial role in the usability of physical objects. 5) Accessibility: While generative AI simplifies 3D model creation, the need for fabrication equipment can limit participation, making AI-assisted creation less inclusive. These five key aspects provide a framework for assessing how well a physical making process aligns with the capabilities of 3D generative AI and values in the world.

comment: ACM CHI Conference on Human Factors in Computing Systems (CHI 2025), Workshop on Generative AI and Human-Computer Interaction, Yokohama, Japan, April 26 to May 1, 2025

♻ ★ Computational Design and Fabrication of Modular Robots with Untethered Control

Manas Bhargava, Takefumi Hiraki, Malina Strugaru, Yuhan Zhang, Michal Piovarci, Chiara Daraio, Daisuke Iwai, Bernd Bickel

Natural organisms utilize distributed actuation through their musculoskeletal systems to adapt their gait for traversing diverse terrains or to morph their bodies for varied tasks. A longstanding challenge in robotics is to emulate this capability of natural organisms, which has motivated the development of numerous soft robotic systems. However, such systems are generally optimized for a single functionality, lack the ability to change form or function on demand, or remain tethered to bulky control systems. To address these limitations, we present a framework for designing and controlling robots that utilize distributed actuation. We propose a novel building block that integrates 3D-printed bones with liquid crystal elastomer (LCE) muscles as lightweight actuators, enabling the modular assembly of musculoskeletal robots. We developed LCE rods that contract in response to infrared radiation, thereby providing localized, untethered control over the distributed skeletal network and producing global deformations of the robot. To fully capitalize on the extensive design space, we introduce two computational tools: one for optimizing the robot's skeletal graph to achieve multiple target deformations, and another for co-optimizing skeletal designs and control gaits to realize desired locomotion. We validate our framework by constructing several robots that demonstrate complex shape morphing, diverse control schemes, and environmental adaptability. Our system integrates advances in modular material building, untethered and distributed control, and computational design to introduce a new generation of robots that brings us closer to the capabilities of living organisms.

♻ ★ Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data

Sanjeev Ramkumar Sudha, Joel Jose, Erlend M. Coates

Robotic information gathering (RIG) techniques refer to methods where mobile robots are used to acquire data about the physical environment with a suite of sensors. Informative planning is an important part of RIG where the goal is to find sequences of actions or paths that maximize efficiency or the quality of information collected. Many existing solutions solve this problem by assuming that the environment is known in advance. However, real environments could be unknown or time-varying, and adaptive informative planning remains an active area of research. Adaptive planning and incremental online mapping are required for mapping initially unknown or varying spatial fields. Gaussian process (GP) regression is a widely used technique in RIG for mapping continuous spatial fields. However, it falls short in many applications as its real-time performance does not scale well to large datasets. To address these challenges, this paper proposes an efficient adaptive informative planning approach for mapping continuous scalar fields with GPs with streaming sparse GPs. Simulation experiments are performed with a synthetic dataset and compared against existing benchmarks. Finally, it is also verified with a real-world dataset to further validate the efficacy of the proposed method. Results show that our method achieves similar mapping accuracy to the baselines while reducing computational complexity for longer missions.

comment: Accepted for presentation at 2025 European Conference on Mobile Robots

♻ ★ Safety-Critical Human-Machine Shared Driving for Vehicle Collision Avoidance based on Hamilton-Jacobi reachability

Shiyue Zhao, Junzhi Zhang, Rui Zhou, Neda Masoud, Jianxiong Li, Helai Huang, Shijie Zhao

Road safety continues to be a pressing global issue, with vehicle collisions imposing significant human, societal, and economic burdens. Human-machine shared collision avoidance in critical collision scenarios aims to aid drivers' accident avoidance through intervening only when necessary. Existing methods count on replanning collision-free trajectories and imposing human-machine tracking, which usually interrupts the driver's intent and increases the risk of conflict. This paper introduces a Reachability-Aware Reinforcement Learning (RL) framework for shared control, guided by Hamilton-Jacobi (HJ) reachability analysis. Machine intervention is activated only when the vehicle approaches the Collision Avoidance Reachable Set (CARS), which represents states where collision is unavoidable. First, we precompute the reachability distributions and the CARS by solving the Bellman equation using offline data. To reduce human-machine conflicts, we develop a driver model for sudden obstacles and propose an authority allocation strategy considering key collision avoidance features. Finally, we train a RL agent to reduce human-machine conflicts while enforcing the hard constraint of avoiding entry into the CARS. The proposed method was tested on a real vehicle platform. Results show that the controller intervenes effectively near CARS to prevent collisions while maintaining improved original driving task performance. Robustness analysis further supports its flexibility across different driver attributes.

comment: 36 pages, 15 figures

♻ ★ YORI: Autonomous Cooking System Utilizing a Modular Robotic Kitchen and a Dual-Arm Proprioceptive Manipulator

Donghun Noh, Hyunwoo Nam, Kyle Gillespie, Yeting Liu, Dennis Hong

This paper presents Yummy Operations Robot Initiative (YORI), a proprioceptive dual-arm robotic system that demonstrates autonomous multi-dish cooking for scalable food service applications. YORI integrates a dual-arm manipulator equipped with proprioceptive actuators, custom-designed tools, appliances, and a structured kitchen environment to address the complexities of cooking tasks. The proprioceptive actuators enable fast, precise, force-controlled movements while mitigating the risks associated with cooking-related impacts. The system's modular kitchen design and flexible tool-changing mechanism support simultaneous multi-dish preparation through torque control and optimization-based motion planning and scheduling. A comprehensive scheduling framework with dynamic rescheduling ensures reliable adaptation to new orders and delays. The system was publicly validated through live demonstrations, reliably preparing steak-frites across multiple convention sessions. This paper details YORI's design and explores future directions in kitchen optimization, task planning, and food quality control, demonstrating its potential as a scalable robotic cooking solution. A system introduction and cooking videos are available online

comment: This work has been submitted to IEEE Robotics & Automation Magazine for possible publication

♻ ★ A Survey on Vision-Language-Action Models for Embodied AI

Yueen Ma, Zixing Song, Yuzheng Zhuang, Jianye Hao, Irwin King

Embodied AI is widely recognized as a key element of artificial general intelligence because it involves controlling embodied agents to perform tasks in the physical world. Building on the success of large language models and vision-language models, a new category of multimodal models -- referred to as vision-language-action models (VLAs) -- has emerged to address language-conditioned robotic tasks in embodied AI by leveraging their distinct ability to generate actions. In recent years, a myriad of VLAs have been developed, making it imperative to capture the rapidly evolving landscape through a comprehensive survey. To this end, we present the first survey on VLAs for embodied AI. This work provides a detailed taxonomy of VLAs, organized into three major lines of research. The first line focuses on individual components of VLAs. The second line is dedicated to developing control policies adept at predicting low-level actions. The third line comprises high-level task planners capable of decomposing long-horizon tasks into a sequence of subtasks, thereby guiding VLAs to follow more general user instructions. Furthermore, we provide an extensive summary of relevant resources, including datasets, simulators, and benchmarks. Finally, we discuss the challenges faced by VLAs and outline promising future directions in embodied AI. We have created a project associated with this survey, which is available at https://github.com/yueen-ma/Awesome-VLA.

comment: Project page: https://github.com/yueen-ma/Awesome-VLA

♻ ★ CRMAgent: A Multi-Agent LLM System for E-Commerce CRM Message Template Generation

Yinzhu Quan, Xinrui Li, Ying Chen

In e-commerce private-domain channels such as instant messaging and e-mail, merchants engage customers directly as part of their Customer Relationship Management (CRM) programmes to drive retention and conversion. While a few top performers excel at crafting outbound messages, most merchants struggle to write persuasive copy because they lack both expertise and scalable tools. We introduce CRMAgent, a multi-agent system built on large language models (LLMs) that generates high-quality message templates and actionable writing guidance through three complementary modes. First, group-based learning enables the agent to learn from a merchant's own top-performing messages within the same audience segment and rewrite low-performing ones. Second, retrieval-and-adaptation fetches templates that share the same audience segment and exhibit high similarity in voucher type and product category, learns their successful patterns, and adapts them to the current campaign. Third, a rule-based fallback provides a lightweight zero-shot rewrite when no suitable references are available. Extensive experiments show that CRMAgent consistently outperforms merchants' original templates, delivering significant gains in both audience-match and marketing-effectiveness metrics.

♻ ★ Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control

Yan Zhang

The increasing penetration of Distributed Energy Resources (DERs), widespread adoption of Electric Vehicles (EVs), and the growing frequency of extreme weather events have significantly increased the complexity of power grid planning, operation, and management. Traditional rule-based systems and numerical optimization approaches often struggle with the scale, dynamics, and adaptability required by modern power networks. This paper introduces Grid-Agent, an autonomous, AI-driven framework that combines Large Language Models (LLMs) with multi-agent reinforcement learning to detect and remediate grid violations in real time. Grid-Agent integrates semantic reasoning with numerical precision through a modular agent architecture: a planning agent generates coordinated action sequences using numerical power flow solvers, while a validation agent evaluates system stability and action effectiveness via sandboxed execution with safety rollbacks. To ensure scalability, Grid-Agent incorporates an adaptive multiscale network representation that dynamically selects optimal encoding schemes based on network size and complexity. The framework enables coordinated violation resolution through optimizing switch configurations, battery deployment, and load curtailment strategies. Experimental results in standard IEEE and CIGRE test systems (IEEE 69-bus, CIGRE MV, and IEEE 30-bus) demonstrate superior violation mitigation performance. Additionally, the framework's built-in data collection and learning capabilities enable continuous learning and adaptation to diverse network topologies. The autonomous nature of the framework makes it particularly suitable for modern smart grid applications requiring rapid response to dynamic operating conditions.

♻ ★ A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

Jinyuan Fang, Yanwen Peng, Xi Zhang, Yingxu Wang, Xinhao Yi, Guibin Zhang, Yi Xu, Bin Wu, Siwei Liu, Zihao Li, Zhaochun Ren, Nikos Aletras, Xi Wang, Han Zhou, Zaiqiao Meng

Recent advances in large language models have sparked growing interest in AI agents capable of solving complex, real-world tasks. However, most existing agent systems rely on manually crafted configurations that remain static after deployment, limiting their ability to adapt to dynamic and evolving environments. To this end, recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback. This emerging direction lays the foundation for self-evolving AI agents, which bridge the static capabilities of foundation models with the continuous adaptability required by lifelong agentic systems. In this survey, we provide a comprehensive review of existing techniques for self-evolving agentic systems. Specifically, we first introduce a unified conceptual framework that abstracts the feedback loop underlying the design of self-evolving agentic systems. The framework highlights four key components: System Inputs, Agent System, Environment, and Optimisers, serving as a foundation for understanding and comparing different strategies. Based on this framework, we systematically review a wide range of self-evolving techniques that target different components of the agent system. We also investigate domain-specific evolution strategies developed for specialised fields such as biomedicine, programming, and finance, where optimisation objectives are tightly coupled with domain constraints. In addition, we provide a dedicated discussion on the evaluation, safety, and ethical considerations for self-evolving agentic systems, which are critical to ensuring their effectiveness and reliability. This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents, laying the foundation for the development of more adaptive, autonomous, and lifelong agentic systems.

comment: Github Repo: https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents

♻ ★ A State-Space Representation of Coupled Linear Multivariate PDEs and Stability Analysis using SDP

Declan S. Jagt, Matthew M. Peet

Physical processes evolving in both time and space are often modeled using Partial Differential Equations (PDEs). Recently, it has been shown how stability analysis and control of coupled PDEs in a single spatial variable can be more conveniently performed using an equivalent Partial Integral Equation (PIE) representation. The construction of this PIE representation is based on an analytic expression for the inverse of the spatial differential operator, $\partial_s^{d}$, on the domain defined by boundary conditions. In this paper, we show how this univariate representation may be extended inductively to multiple spatial variables by representing the domain as the intersection of lifted univariate domains. Specifically, we show that if each univariate domain is well-posed, then there exists a readily verified consistency condition which is necessary and sufficient for existence of an inverse to the multivariate spatial differential operator, $D^\alpha=\partial_{s_1}^{\alpha_1}\cdots\partial_{s_N}^{\alpha_N}$, on the PDE domain. Furthermore, we show that this inverse is an element of a $*$-algebra of Partial Integral (PI) operators defined by polynomial semi-separable kernels. Based on this operator algebra, we show that the evolution of any suitably well-posed linear multivariate PDE may be described by a PIE, parameterized by elements of the PI algebra. A convex computational test for PDE stability is then proposed using a positive matrix parameterization of positive PI operators, and software (PIETOOLS) is provided which automates the process of representation and stability analysis of such PDEs. This software is used to analyze stability of 2D heat, wave, and plate equations, obtaining accurate bounds on the rate of decay.

♻ ★ A State-Space Representation of Coupled Linear Multivariate PDEs and Stability Analysis using SDP

Declan S. Jagt, Matthew M. Peet

Physical processes evolving in both time and space are often modeled using Partial Differential Equations (PDEs). Recently, it has been shown how stability analysis and control of coupled PDEs in a single spatial variable can be more conveniently performed using an equivalent Partial Integral Equation (PIE) representation. The construction of this PIE representation is based on an analytic expression for the inverse of the spatial differential operator, $\partial_s^{d}$, on the domain defined by boundary conditions. In this paper, we show how this univariate representation may be extended inductively to multiple spatial variables by representing the domain as the intersection of lifted univariate domains. Specifically, we show that if each univariate domain is well-posed, then there exists a readily verified consistency condition which is necessary and sufficient for existence of an inverse to the multivariate spatial differential operator, $D^\alpha=\partial_{s_1}^{\alpha_1}\cdots\partial_{s_N}^{\alpha_N}$, on the PDE domain. Furthermore, we show that this inverse is an element of a $*$-algebra of Partial Integral (PI) operators defined by polynomial semi-separable kernels. Based on this operator algebra, we show that the evolution of any suitably well-posed linear multivariate PDE may be described by a PIE, parameterized by elements of the PI algebra. A convex computational test for PDE stability is then proposed using a positive matrix parameterization of positive PI operators, and software (PIETOOLS) is provided which automates the process of representation and stability analysis of such PDEs. This software is used to analyze stability of 2D heat, wave, and plate equations, obtaining accurate bounds on the rate of decay.

♻ ★ Probabilistic Reachable Set Estimation for Saturated Systems with Unbounded Additive Disturbances

Carlo Karam, Matteo Tacchi-Bénard, Mirko Fiacchini

In this paper, we present an analytical approach for the synthesis of ellipsoidal probabilistic reachable sets of saturated systems subject to unbounded additive noise. Using convex optimization methods, we compute a contraction factor of the saturated error dynamics that allows us to tightly bound its evolution and therefore construct accurate reachable sets. The proposed approach is applicable to independent, zero mean disturbances with a known covariance. A numerical example illustrates the applicability and effectiveness of the proposed design.

comment: 11 pages, LaTeX; section III.C framing rephrased, Remark 3 added, numerical example presentation updated, typos corrected, references updated

♻ ★ Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control

Yan Zhang

The increasing penetration of Distributed Energy Resources (DERs), widespread adoption of Electric Vehicles (EVs), and the growing frequency of extreme weather events have significantly increased the complexity of power grid planning, operation, and management. Traditional rule-based systems and numerical optimization approaches often struggle with the scale, dynamics, and adaptability required by modern power networks. This paper introduces Grid-Agent, an autonomous, AI-driven framework that combines Large Language Models (LLMs) with multi-agent reinforcement learning to detect and remediate grid violations in real time. Grid-Agent integrates semantic reasoning with numerical precision through a modular agent architecture: a planning agent generates coordinated action sequences using numerical power flow solvers, while a validation agent evaluates system stability and action effectiveness via sandboxed execution with safety rollbacks. To ensure scalability, Grid-Agent incorporates an adaptive multiscale network representation that dynamically selects optimal encoding schemes based on network size and complexity. The framework enables coordinated violation resolution through optimizing switch configurations, battery deployment, and load curtailment strategies. Experimental results in standard IEEE and CIGRE test systems (IEEE 69-bus, CIGRE MV, and IEEE 30-bus) demonstrate superior violation mitigation performance. Additionally, the framework's built-in data collection and learning capabilities enable continuous learning and adaptation to diverse network topologies. The autonomous nature of the framework makes it particularly suitable for modern smart grid applications requiring rapid response to dynamic operating conditions.

♻ ★ A Review of Hydrogen-Enabled Resilience Enhancement for Multi-Energy Systems

Liang Yu, Haoyu Fang, Goran Strbac, Dawei Qiu, Dong Yue, Xiaohong Guan, Gerhard P. Hancke

Ensuring resilience in multi-energy systems (MESs) becomes both more urgent and more challenging due to the rising occurrence and severity of extreme events (e.g., natural disasters, extreme weather, and cyber-physical attacks). Among many measures of strengthening MES resilience, the integration of hydrogen shows exceptional potential in cross-temporal flexibility, cross-spatial flexibility, cross-sector flexibility, and black start capability. Although many hydrogen-enabled MES resilience enhancement measures have been developed, the current literature lacks a systematic overview of hydrogen-enabled resilience enhancement in MESs. To fill the research gap, this paper provides a comprehensive overview of hydrogen-enabled MES resilience enhancement. First, advantages and challenges of adopting hydrogen in MES resilience enhancement are summarized. Then, we propose a resilience enhancement framework for hydrogen-enabled MESs. Under the proposed framework, existing resilience metrics and event-oriented contingency models are summarized and discussed. Furthermore, we classify hydrogen-enabled planning measures by the types of hydrogen-related facilities and provide some insights for planning problem formulation frameworks. Moreover, we categorize the hydrogen-enabled operation enhancement measures into three operation response stages: preventive, emergency, and restoration. Finally, we identify some research gaps and point out possible future directions in aspects of comprehensive resilience metric design, temporally-correlated event-targeted scenario generation, multi-type temporal-spatial cyber-physical contingency modeling under compound extreme events, multi-network multi-timescale coordinated planning and operation, low-carbon resilient planning and operation, and large language model-assisted whole-process resilience enhancement.

comment: 28 pages, 14 figures

♻ ★ Safety-Critical Human-Machine Shared Driving for Vehicle Collision Avoidance based on Hamilton-Jacobi reachability

Shiyue Zhao, Junzhi Zhang, Rui Zhou, Neda Masoud, Jianxiong Li, Helai Huang, Shijie Zhao

Road safety continues to be a pressing global issue, with vehicle collisions imposing significant human, societal, and economic burdens. Human-machine shared collision avoidance in critical collision scenarios aims to aid drivers' accident avoidance through intervening only when necessary. Existing methods count on replanning collision-free trajectories and imposing human-machine tracking, which usually interrupts the driver's intent and increases the risk of conflict. This paper introduces a Reachability-Aware Reinforcement Learning (RL) framework for shared control, guided by Hamilton-Jacobi (HJ) reachability analysis. Machine intervention is activated only when the vehicle approaches the Collision Avoidance Reachable Set (CARS), which represents states where collision is unavoidable. First, we precompute the reachability distributions and the CARS by solving the Bellman equation using offline data. To reduce human-machine conflicts, we develop a driver model for sudden obstacles and propose an authority allocation strategy considering key collision avoidance features. Finally, we train a RL agent to reduce human-machine conflicts while enforcing the hard constraint of avoiding entry into the CARS. The proposed method was tested on a real vehicle platform. Results show that the controller intervenes effectively near CARS to prevent collisions while maintaining improved original driving task performance. Robustness analysis further supports its flexibility across different driver attributes.

comment: 36 pages, 15 figures

♻ ★ SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes

Yunpeng Mei, Hongjie Cao, Yinqiu Xia, Wei Xiao, Zhaohan Feng, Gang Wang, Jie Chen

Real-time interactive grasp synthesis for dynamic objects remains challenging as existing methods fail to achieve low-latency inference while maintaining promptability. To bridge this gap, we propose SPGrasp (spatiotemporal prompt-driven dynamic grasp synthesis), a novel framework extending segment anything model v2 (SAMv2) for video stream grasp estimation. Our core innovation integrates user prompts with spatiotemporal context, enabling real-time interaction with end-to-end latency as low as 59 ms while ensuring temporal consistency for dynamic objects. In benchmark evaluations, SPGrasp achieves instance-level grasp accuracies of 90.6% on OCID and 93.8% on Jacquard. On the challenging GraspNet-1Billion dataset under continuous tracking, SPGrasp achieves 92.0% accuracy with 73.1 ms per-frame latency, representing a 58.5% reduction compared to the prior state-of-the-art promptable method RoG-SAM while maintaining competitive accuracy. Real-world experiments involving 13 moving objects demonstrate a 94.8% success rate in interactive grasping scenarios. These results confirm SPGrasp effectively resolves the latency-interactivity trade-off in dynamic grasp synthesis.

♻ ★ LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing

Changyi Lin, Yuxin Ray Song, Boda Huo, Mingyang Yu, Yikai Wang, Shiqi Liu, Yuxiang Yang, Wenhao Yu, Tingnan Zhang, Jie Tan, Yiyue Luo, Ding Zhao

Quadrupedal robots have demonstrated remarkable agility and robustness in traversing complex terrains. However, they struggle with dynamic object interactions, where contact must be precisely sensed and controlled. To bridge this gap, we present LocoTouch, a system that equips quadrupedal robots with tactile sensing to address a particularly challenging task in this category: long-distance transport of unsecured cylindrical objects, which typically requires custom mounting or fastening mechanisms to maintain stability. For efficient large-area tactile sensing, we design a high-density distributed tactile sensor that covers the entire back of the robot. To effectively leverage tactile feedback for robot control, we develop a simulation environment with high-fidelity tactile signals, and train tactile-aware transport policies using a two-stage learning pipeline. Furthermore, we design a novel reward function to promote robust, symmetric, and frequency-adaptive locomotion gaits. After training in simulation, LocoTouch transfers zero-shot to the real world, reliably transporting a wide range of unsecured cylindrical objects with diverse sizes, weights, and surface properties. Moreover, it remains robust over long distances, on uneven terrain, and under severe perturbations.

comment: Project page: https://linchangyi1.github.io/LocoTouch

♻ ★ Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics

Tyler Han, Preet Shah, Sidharth Rajagopal, Yanda Bao, Sanghun Jung, Sidharth Talia, Gabriel Guo, Bryan Xu, Bhaumik Mehta, Emma Romig, Rosario Scalise, Byron Boots

Reinforcement Learning (RL) has been pivotal in recent robotics milestones and is poised to play a prominent role in the future. However, these advances can rely on proprietary simulators, expensive hardware, and a daunting range of tools and skills. As a result, broader communities are disconnecting from the state-of-the-art; education curricula are poorly equipped to teach indispensable modern robotics skills involving hardware, deployment, and iterative development. To address this gap between the broader and scientific communities, we contribute Wheeled Lab, an ecosystem which integrates accessible, open-source wheeled robots with Isaac Lab, an open-source robot learning and simulation framework, that is widely adopted in the state-of-the-art. To kickstart research and education, this work demonstrates three state-of-the-art zero-shot policies for small-scale RC cars developed through Wheeled Lab: controlled drifting, elevation traversal, and visual navigation. The full stack, from hardware to software, is low-cost and open-source. Videos and additional materials can be found at: https://uwrobotlearning.github.io/WheeledLab/

comment: To appear at Conference on Robot Learning, 2025

♻ ★ ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation

Enyu Zhao, Vedant Raval, Hejia Zhang, Jiageng Mao, Zeyu Shangguan, Stefanos Nikolaidis, Yue Wang, Daniel Seita

Vision-Language Models (VLMs) have revolutionized artificial intelligence and robotics due to their commonsense reasoning capabilities. In robotic manipulation, VLMs are used primarily as high-level planners, but recent work has also studied their lower-level reasoning ability, which refers to making decisions about precise robot movements. However, the community currently lacks a clear and common benchmark that can evaluate how well VLMs can aid low-level reasoning in robotics. Consequently, we propose a novel benchmark, ManipBench, to evaluate the low-level robot manipulation reasoning capabilities of VLMs across various dimensions, including how well they understand object-object interactions and deformable object manipulation. We extensively test 33 representative VLMs across 10 model families on our benchmark, including variants to test different model sizes. Our evaluation shows that the performance of VLMs significantly varies across tasks, and there is a strong correlation between this performance and trends in our real-world manipulation tasks. It also shows that there remains a significant gap between these models and human-level understanding. See our website at: https://manipbench.github.io.

comment: Conference on Robot Learning (CoRL) 2025. 50 pages and 30 figures. v2 is the camera-ready and includes a few more new experiments compared to v1

♻ ★ RobotxR1: Enabling Embodied Robotic Intelligence on Large Language Models through Closed-Loop Reinforcement Learning

Liam Boyle, Nicolas Baumann, Paviththiren Sivasothilingam, Michele Magno, Luca Benini

Future robotic systems operating in real-world environments will require on-board embodied intelligence without continuous cloud connection, balancing capabilities with constraints on computational power and memory. This work presents an extension of the R1-zero approach, which enables the usage of low parameter-count Large Language Models (LLMs) in the robotic domain. The R1-Zero approach was originally developed to enable mathematical reasoning in LLMs using static datasets. We extend it to the robotics domain through integration in a closed-loop Reinforcement Learning (RL) framework. This extension enhances reasoning in Embodied Artificial Intelligence (Embodied AI) settings without relying solely on distillation of large models through Supervised Fine-Tuning (SFT). We show that small-scale LLMs can achieve effective reasoning performance by learning through closed-loop interaction with their environment, which enables tasks that previously required significantly larger models. In an autonomous driving setting, a performance gain of 20.2%-points over the SFT-based baseline is observed with a Qwen2.5-1.5B model. Using the proposed training procedure, Qwen2.5-3B achieves a 63.3% control adaptability score, surpassing the 58.5% obtained by the much larger, cloud-bound GPT-4o. These results highlight that practical, on-board deployment of small LLMs is not only feasible but can outperform larger models if trained through environmental feedback, underscoring the importance of an interactive learning framework for robotic Embodied AI, one grounded in practical experience rather than static supervision.

♻ ★ Multi Object Tracking for Predictive Collision Avoidance

Bruk Gebregziabher, Hadush Hailu

The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multi-object tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

♻ ★ CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks

Yixuan Li, Yutang Lin, Jieming Cui, Tengyu Liu, Wei Liang, Yixin Zhu, Siyuan Huang

Humanoid teleoperation plays a vital role in demonstrating and collecting data for complex humanoid-scene interactions. However, current teleoperation systems face critical limitations: they decouple upper- and lower-body control to maintain stability, restricting natural coordination, and operate open-loop without real-time position feedback, leading to accumulated drift. The fundamental challenge is achieving precise, coordinated whole-body teleoperation over extended durations while maintaining accurate global positioning. Here we show that an MoE-based teleoperation system, CLONE, with closed-loop error correction enables unprecedented whole-body teleoperation fidelity, maintaining minimal positional drift over long-range trajectories using only head and hand tracking from an MR headset. Unlike previous methods that either sacrifice coordination for stability or suffer from unbounded drift, CLONE learns diverse motion skills while preventing tracking error accumulation through real-time feedback, enabling complex coordinated movements such as ``picking up objects from the ground.'' These results establish a new milestone for whole-body humanoid teleoperation for long-horizon humanoid-scene interaction tasks.

comment: 18 pages, 13 figures

♻ ★ Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams

Zhi Su, Yuman Gao, Emily Lukas, Yunfei Li, Jiaze Cai, Faris Tulbah, Fei Gao, Chao Yu, Zhongyu Li, Yi Wu, Koushil Sreenath

Achieving coordinated teamwork among legged robots requires both fine-grained locomotion control and long-horizon strategic decision-making. Robot soccer offers a compelling testbed for this challenge, combining dynamic, competitive, and multi-agent interactions. In this work, we present a hierarchical multi-agent reinforcement learning (MARL) framework that enables fully autonomous and decentralized quadruped robot soccer. First, a set of highly dynamic low-level skills is trained for legged locomotion and ball manipulation, such as walking, dribbling, and kicking. On top of these, a high-level strategic planning policy is trained with Multi-Agent Proximal Policy Optimization (MAPPO) via Fictitious Self-Play (FSP). This learning framework allows agents to adapt to diverse opponent strategies and gives rise to sophisticated team behaviors, including coordinated passing, interception, and dynamic role allocation. With an extensive ablation study, the proposed learning method shows significant advantages in the cooperative and competitive multi-agent soccer game. We deploy the learned policies to real quadruped robots relying solely on onboard proprioception and decentralized localization, with the resulting system supporting autonomous robot-robot and robot-human soccer matches on indoor and outdoor soccer courts.

comment: 11 pages, 12 figures, CoRL 2025

♻ ★ Domain-Conditioned Scene Graphs for State-Grounded Task Planning IROS 2025

Jonas Herzog, Jiangpin Liu, Yue Wang

Recent robotic task planning frameworks have integrated large multimodal models (LMMs) such as GPT-4o. To address grounding issues of such models, it has been suggested to split the pipeline into perceptional state grounding and subsequent state-based planning. As we show in this work, the state grounding ability of LMM-based approaches is still limited by weaknesses in granular, structured, domain-specific scene understanding. To address this shortcoming, we develop a more structured state grounding framework that features a domain-conditioned scene graph as its scene representation. We show that such representation is actionable in nature as it is directly mappable to a symbolic state in planning languages such as the Planning Domain Definition Language (PDDL). We provide an instantiation of our state grounding framework where the domain-conditioned scene graph generation is implemented with a lightweight vision-language approach that classifies domain-specific predicates on top of domain-relevant object detections. Evaluated across three domains, our approach achieves significantly higher state rounding accuracy and task planning success rates compared to LMM-based approaches.

comment: Accepted for IROS 2025

♻ ★ Distributed Safety-Critical MPC for Multi-Agent Formation Control and Obstacle Avoidance

Chao Wang, Shuyuan Zhang, Lei Wang

For nonlinear multi-agent systems with high relative degrees, achieving formation control and obstacle avoidance in a distributed manner remains a significant challenge. To address this issue, we propose a novel distributed safety-critical model predictive control (DSMPC) algorithm that incorporates discrete-time high-order control barrier functions (DHCBFs) to enforce safety constraints, alongside discrete-time control Lyapunov functions (DCLFs) to establish terminal constraints. To facilitate distributed implementation, we develop estimated neighbor states for formulating DHCBFs and DCLFs, while also devising a bound constraint to limit estimation errors and ensure convergence. Additionally, we provide theoretical guarantees regarding the feasibility and stability of the proposed DSMPC algorithm based on a mild assumption. The effectiveness of the proposed method is evidenced by the simulation results, demonstrating improved performance and reduced computation time compared to existing approaches.

comment: Accepted for presentation at the 64th IEEE Conference on Decision and Control (CDC 2025)

♻ ★ Distributed Safety-Critical MPC for Multi-Agent Formation Control and Obstacle Avoidance

Chao Wang, Shuyuan Zhang, Lei Wang

For nonlinear multi-agent systems with high relative degrees, achieving formation control and obstacle avoidance in a distributed manner remains a significant challenge. To address this issue, we propose a novel distributed safety-critical model predictive control (DSMPC) algorithm that incorporates discrete-time high-order control barrier functions (DHCBFs) to enforce safety constraints, alongside discrete-time control Lyapunov functions (DCLFs) to establish terminal constraints. To facilitate distributed implementation, we develop estimated neighbor states for formulating DHCBFs and DCLFs, while also devising a bound constraint to limit estimation errors and ensure convergence. Additionally, we provide theoretical guarantees regarding the feasibility and stability of the proposed DSMPC algorithm based on a mild assumption. The effectiveness of the proposed method is evidenced by the simulation results, demonstrating improved performance and reduced computation time compared to existing approaches.

comment: Accepted for presentation at the 64th IEEE Conference on Decision and Control (CDC 2025)

♻ ★ Physics-Informed Unit Commitment Framework for Nuclear Reactors

Shiny Choudhury, Michael Davidson, George Tynan

Nuclear reactors are often modeled as inflexible baseload generators with fixed downtimes and restrictive ramping constraints. In practice, however, a reactor's operational flexibility is closely tied to its fuel cycle and associated reactivity margin. A key physical constraint for power maneuverability is xenon poisoning, caused from the transient buildup of neutron-absorbing xenon following a power reduction. This transient can delay or prevent subsequent power ramp-up due to suppressed core reactivity. Additionally, if a reactor is shutdown during periods of low reactivity, restart times can vary significantly, leading to prolonged downtimes. This work introduces a physics-informed modeling framework that embeds fuel cycle dynamics within a unit commitment (UC) formulation. The framework tracks reactivity margin, dynamically enforces xenon induced constraints, and endogenously schedules refueling outages based on core conditions. By capturing intracycle reactivity evolution, the model enables operation dependent nuclear dispatch that reflects both techno-economic requirements and irreducible nuclear physics limits. Application to a representative reactor fleet shows that flexible operation can slow reactivity degradation and extend fuel cycles. Results further demonstrate that different operational modes substantially affect VRE utilization, curtailment, and nuclear fleet capacity factors. These findings highlight the importance of fuel cycle aware flexibility modeling for accurate reactor scheduling and integration of nuclear power into energy system models.

comment: 10 pages, Code: https://github.com/shinychoudhury/physics-informed-nuclear-reactor-unit-commitment-algorithm

♻ ★ Robustness Analysis for Quantum Systems Controlled by Continuous-Time Pulses

Sean Patrick O'Neil, Edmond Jonckheere, Sophie Schirmer

Differential sensitivity techniques originally developed to study the robustness of energy landscape controllers are generalized to the important case of closed quantum systems subject to continuously varying controls. Vanishing sensitivity to parameter variation is shown to coincide with perfect fidelity, as was the case for time-invariant controls. Upper bounds on the magnitude of the differential sensitivity to any parameter variation are derived based simply on knowledge of the system Hamiltonian and the maximum size of the control inputs.

comment: 6 pages, 2 figures

♻ ★ Topology Inference for Network Systems with Unknown Inputs

Qing Jiao, Yushan Li, Jianping He

Topology inference is a powerful tool to better understand the behaviours of network systems (NSs). Different from most of prior works, this paper is dedicated to inferring the directed topology of NSs from noisy observations, where the nodes are influenced by unknown time-varying inputs. These inputs can be actively injected signals by the user, intrinsic system noises or extrinsic environment interference. To tackle this challenging problem, we propose a two-stage inference scheme to overcome the influence of the inputs. First, by leveraging the second-order difference of the state evolution, we establish a judging criterion to detect the input injection time and provide the probability guarantees. With this injection time to determine available observations, an initial topology is accordingly inferred to further facilitate the input estimation. Second, utilizing the stability characteristic of the system response, a recursive input filtering algorithm is designed to approximate the zero-input response, which directly reflects the topology structure. Then, we construct a decreasing-weight based optimization problem to infer the final network topology from the approximated response. Comprehensive simulations demonstrate the effectiveness of the proposed method.

comment: This latest version of this paper was accepted by and presented at 2025 American Control Conference

♻ ★ Multi Object Tracking for Predictive Collision Avoidance

Bruk Gebregziabher, Hadush Hailu

The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multi-object tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

♻ ★ Topology Inference for Network Systems with Unknown Inputs

Qing Jiao, Yushan Li, Jianping He

Topology inference is a powerful tool to better understand the behaviours of network systems (NSs). Different from most of prior works, this paper is dedicated to inferring the directed topology of NSs from noisy observations, where the nodes are influenced by unknown time-varying inputs. These inputs can be actively injected signals by the user, intrinsic system noises or extrinsic environment interference. To tackle this challenging problem, we propose a two-stage inference scheme to overcome the influence of the inputs. First, by leveraging the second-order difference of the state evolution, we establish a judging criterion to detect the input injection time and provide the probability guarantees. With this injection time to determine available observations, an initial topology is accordingly inferred to further facilitate the input estimation. Second, utilizing the stability characteristic of the system response, a recursive input filtering algorithm is designed to approximate the zero-input response, which directly reflects the topology structure. Then, we construct a decreasing-weight based optimization problem to infer the final network topology from the approximated response. Comprehensive simulations demonstrate the effectiveness of the proposed method.

comment: This latest version of this paper was accepted by and presented at 2025 American Control Conference

♻ ★ Investigating Tax Evasion Emergence Using Dual Large Language Model and Deep Reinforcement Learning Powered Agent-based Simulation

Teddy Lazebnik, Labib Shami

Tax evasion, usually the largest component of an informal economy, is a persistent challenge over history with significant socio-economic implications. Many socio-economic studies investigate its dynamics, including influencing factors, the role and influence of taxation policies, and the prediction of the tax evasion volume over time. These studies assumed such behavior is given, as observed in the real world, neglecting the "big bang" of such activity in a population. To this end, computational economy studies adopted developments in computer simulations, in general, and recent innovations in artificial intelligence (AI), in particular, to simulate and study informal economy appearance in various socio-economic settings. This study presents a novel computational framework to examine the dynamics of tax evasion and the emergence of informal economic activity. Employing an agent-based simulation powered by Large Language Models and Deep Reinforcement Learning, the framework is uniquely designed to allow informal economic behaviors to emerge organically, without presupposing their existence or explicitly signaling agents about the possibility of evasion. This provides a rigorous approach for exploring the socio-economic determinants of compliance behavior. The experimental design, comprising model validation and exploratory phases, demonstrates the framework's robustness in replicating theoretical economic behaviors. Findings indicate that individual personality traits, external narratives, enforcement probabilities, and the perceived efficiency of public goods provision significantly influence both the timing and extent of informal economic activity. The results underscore that efficient public goods provision and robust enforcement mechanisms are complementary; neither alone is sufficient to curtail informal activity effectively.

★ Tree-Guided Diffusion Planner

Hyeonseong Jeon, Cheolhong Min, Jaesik Park

Planning with pretrained diffusion models has emerged as a promising approach for solving test-time guided control problems. However, standard gradient guidance typically performs optimally under convex and differentiable reward landscapes, showing substantially reduced effectiveness in real-world scenarios involving non-convex objectives, non-differentiable constraints, and multi-reward structures. Furthermore, recent supervised planning approaches require task-specific training or value estimators, which limits test-time flexibility and zero-shot generalization. We propose a Tree-guided Diffusion Planner (TDP), a zero-shot test-time planning framework that balances exploration and exploitation through structured trajectory generation. We frame test-time planning as a tree search problem using a bi-level sampling process: (1) diverse parent trajectories are produced via training-free particle guidance to encourage broad exploration, and (2) sub-trajectories are refined through fast conditional denoising guided by task objectives. TDP addresses the limitations of gradient guidance by exploring diverse trajectory regions and harnessing gradient information across this expanded solution space using only pretrained models and test-time reward signals. We evaluate TDP on three diverse tasks: maze gold-picking, robot arm block manipulation, and AntMaze multi-goal exploration. TDP consistently outperforms state-of-the-art approaches on all tasks. The project page can be found at: tree-diffusion-planner.github.io.

comment: 20 pages, 11 figures, 14 tables (main paper + appendix) / under review / project page will be available after the paper becomes public in arxiv

★ Can a mobile robot learn from a pedestrian model to prevent the sidewalk salsa?

Olger Siebinga, David Abbink

Pedestrians approaching each other on a sidewalk sometimes end up in an awkward interaction known as the "sidewalk salsa": they both (repeatedly) deviate to the same side to avoid a collision. This provides an interesting use case to study interactions between pedestrians and mobile robots because, in the vast majority of cases, this phenomenon is avoided through a negotiation based on implicit communication. Understanding how it goes wrong and how pedestrians end up in the sidewalk salsa will therefore provide insight into the implicit communication. This understanding can be used to design safe and acceptable robotic behaviour. In a previous attempt to gain this understanding, a model of pedestrian behaviour based on the Communication-Enabled Interaction (CEI) framework was developed that can replicate the sidewalk salsa. However, it is unclear how to leverage this model in robotic planning and decision-making since it violates the assumptions of game theory, a much-used framework in planning and decision-making. Here, we present a proof-of-concept for an approach where a Reinforcement Learning (RL) agent leverages the model to learn how to interact with pedestrians. The results show that a basic RL agent successfully learned to interact with the CEI model. Furthermore, a risk-averse RL agent that had access to the perceived risk of the CEI model learned how to effectively communicate its intention through its motion and thereby substantially lowered the perceived risk, and displayed effort by the modelled pedestrian. These results show this is a promising approach and encourage further exploration.

★ Robust Convex Model Predictive Control with collision avoidance guarantees for robot manipulators

Bernhard Wullt, Johannes Köhler, Per Mattsson, Mikeal Norrlöf, Thomas B. Schön

Industrial manipulators are normally operated in cluttered environments, making safe motion planning important. Furthermore, the presence of model-uncertainties make safe motion planning more difficult. Therefore, in practice the speed is limited in order to reduce the effect of disturbances. There is a need for control methods that can guarantee safe motions that can be executed fast. We address this need by suggesting a novel model predictive control (MPC) solution for manipulators, where our two main components are a robust tube MPC and a corridor planning algorithm to obtain collision-free motion. Our solution results in a convex MPC, which we can solve fast, making our method practically useful. We demonstrate the efficacy of our method in a simulated environment with a 6 DOF industrial robot operating in cluttered environments with uncertainties in model parameters. We outperform benchmark methods, both in terms of being able to work under higher levels of model uncertainties, while also yielding faster motion.

★ A-MHA*: Anytime Multi-Heuristic A*

Ramkumar Natarajan, Muhammad Suhail Saleem, William Xiao, Sandip Aine, Howie Choset, Maxim Likhachev

Designing good heuristic functions for graph search requires adequate domain knowledge. It is often easy to design heuristics that perform well and correlate with the underlying true cost-to-go values in certain parts of the search space but these may not be admissible throughout the domain thereby affecting the optimality guarantees of the search. Bounded suboptimal search using several such partially good but inadmissible heuristics was developed in Multi-Heuristic A* (MHA*). Although MHA* leverages multiple inadmissible heuristics to potentially generate a faster suboptimal solution, the original version does not improve the solution over time. It is a one shot algorithm that requires careful setting of inflation factors to obtain a desired one time solution. In this work, we tackle this issue by extending MHA* to an anytime version that finds a feasible suboptimal solution quickly and continually improves it until time runs out. Our work is inspired from the Anytime Repairing A* (ARA*) algorithm. We prove that our precise adaptation of ARA* concepts in the MHA* framework preserves the original suboptimal and completeness guarantees and enhances MHA* to perform in an anytime fashion. Furthermore, we report the performance of A-MHA* in 3-D path planning domain and sliding tiles puzzle and compare against MHA* and other anytime algorithms.

★ The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics

Nicolas Soncini, Javier Cremona, Erica Vidal, Maximiliano García, Gastón Castro, Taihú Pire

We present a multi-modal dataset collected in a soybean crop field, comprising over two hours of recorded data from sensors such as stereo infrared camera, color camera, accelerometer, gyroscope, magnetometer, GNSS (Single Point Positioning, Real-Time Kinematic and Post-Processed Kinematic), and wheel odometry. This dataset captures key challenges inherent to robotics in agricultural environments, including variations in natural lighting, motion blur, rough terrain, and long, perceptually aliased sequences. By addressing these complexities, the dataset aims to support the development and benchmarking of advanced algorithms for localization, mapping, perception, and navigation in agricultural robotics. The platform and data collection system is designed to meet the key requirements for evaluating multi-modal SLAM systems, including hardware synchronization of sensors, 6-DOF ground truth and loops on long trajectories. We run multimodal state-of-the art SLAM methods on the dataset, showcasing the existing limitations in their application on agricultural settings. The dataset and utilities to work with it are released on https://cifasis.github.io/rosariov2/.

comment: First published on The International Journal of Robotics Research: https://journals.sagepub.com/doi/10.1177/02783649251368909

★ Learning Agile Gate Traversal via Analytical Optimal Policy Gradient

Tianchen Sun, Bingheng Wang, Longbin Tang, Yichao Gao, Lin Zhao

Traversing narrow gates presents a significant challenge and has become a standard benchmark for evaluating agile and precise quadrotor flight. Traditional modularized autonomous flight stacks require extensive design and parameter tuning, while end-to-end reinforcement learning (RL) methods often suffer from low sample efficiency and limited interpretability. In this work, we present a novel hybrid framework that adaptively fine-tunes model predictive control (MPC) parameters online using outputs from a neural network (NN) trained offline. The NN jointly predicts a reference pose and cost-function weights, conditioned on the coordinates of the gate corners and the current drone state. To achieve efficient training, we derive analytical policy gradients not only for the MPC module but also for an optimization-based gate traversal detection module. Furthermore, we introduce a new formulation of the attitude tracking error that admits a simplified representation, facilitating effective learning with bounded gradients. Hardware experiments demonstrate that our method enables fast and accurate quadrotor traversal through narrow gates in confined environments. It achieves several orders of magnitude improvement in sample efficiency compared to naive end-to-end RL approaches.

comment: 8 pages, 8 figures

★ Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler

Liding Zhang, Kuanqi Cai, Yu Zhang, Zhenshan Bing, Chaoqun Wang, Fan Wu, Sami Haddadin, Alois Knoll

Path planning in robotics often involves solving continuously valued, high-dimensional problems. Popular informed approaches include graph-based searches, such as A*, and sampling-based methods, such as Informed RRT*, which utilize informed set and anytime strategies to expedite path optimization incrementally. Informed sampling-based planners define informed sets as subsets of the problem domain based on the current best solution cost. However, when no solution is found, these planners re-sample and explore the entire configuration space, which is time-consuming and computationally expensive. This article introduces Multi-Informed Trees (MIT*), a novel planner that constructs estimated informed sets based on prior admissible solution costs before finding the initial solution, thereby accelerating the initial convergence rate. Moreover, MIT* employs an adaptive sampler that dynamically adjusts the sampling strategy based on the exploration process. Furthermore, MIT* utilizes length-related adaptive sparse collision checks to guide lazy reverse search. These features enhance path cost efficiency and computation times while ensuring high success rates in confined scenarios. Through a series of simulations and real-world experiments, it is confirmed that MIT* outperforms existing single-query, sampling-based planners for problems in R^4 to R^16 and has been successfully applied to real-world robot manipulation tasks. A video showcasing our experimental results is available at: https://youtu.be/30RsBIdexTU

★ Complete Gaussian Splats from a Single Image with Denoising Diffusion Models

Ziwei Liao, Mohamed Sayed, Steven L. Waslander, Sara Vicente, Daniyar Turmukhambetov, Michael Firman

Gaussian splatting typically requires dense observations of the scene and can fail to reconstruct occluded and unobserved areas. We propose a latent diffusion model to reconstruct a complete 3D scene with Gaussian splats, including the occluded parts, from only a single image during inference. Completing the unobserved surfaces of a scene is challenging due to the ambiguity of the plausible surfaces. Conventional methods use a regression-based formulation to predict a single "mode" for occluded and out-of-frustum surfaces, leading to blurriness, implausibility, and failure to capture multiple possible explanations. Thus, they often address this problem partially, focusing either on objects isolated from the background, reconstructing only visible surfaces, or failing to extrapolate far from the input views. In contrast, we propose a generative formulation to learn a distribution of 3D representations of Gaussian splats conditioned on a single input image. To address the lack of ground-truth training data, we propose a Variational AutoReconstructor to learn a latent space only from 2D images in a self-supervised manner, over which a diffusion model is trained. Our method generates faithful reconstructions and diverse samples with the ability to complete the occluded surfaces for high-quality 360-degree renderings.

comment: Main paper: 11 pages; Supplementary materials: 7 pages

★ Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting

Pierrick Lorang, Hong Lu, Johannes Huemer, Patrik Zips, Matthias Scheutz

Imitation learning enables intelligent systems to acquire complex behaviors with minimal supervision. However, existing methods often focus on short-horizon skills, require large datasets, and struggle to solve long-horizon tasks or generalize across task variations and distribution shifts. We propose a novel neuro-symbolic framework that jointly learns continuous control policies and symbolic domain abstractions from a few skill demonstrations. Our method abstracts high-level task structures into a graph, discovers symbolic rules via an Answer Set Programming solver, and trains low-level controllers using diffusion policy imitation learning. A high-level oracle filters task-relevant information to focus each controller on a minimal observation and action space. Our graph-based neuro-symbolic framework enables capturing complex state transitions, including non-spatial and temporal relations, that data-driven learning or clustering techniques often fail to discover in limited demonstration datasets. We validate our approach in six domains that involve four robotic arms, Stacking, Kitchen, Assembly, and Towers of Hanoi environments, and a distinct Automated Forklift domain with two environments. The results demonstrate high data efficiency with as few as five skill demonstrations, strong zero- and few-shot generalizations, and interpretable decision making.

comment: Accepted at CoRL 2025; to appear in PMLR

★ Assessing Human Cooperation for Enhancing Social Robot Navigation

Hariharan Arunachalam, Phani Teja Singamaneni, Rachid Alami

Socially aware robot navigation is a planning paradigm where the robot navigates in human environments and tries to adhere to social constraints while interacting with the humans in the scene. These navigation strategies were further improved using human prediction models, where the robot takes the potential future trajectory of humans while computing its own. Though these strategies significantly improve the robot's behavior, it faces difficulties from time to time when the human behaves in an unexpected manner. This happens as the robot fails to understand human intentions and cooperativeness, and the human does not have a clear idea of what the robot is planning to do. In this paper, we aim to address this gap through effective communication at an appropriate time based on a geometric analysis of the context and human cooperativeness in head-on crossing scenarios. We provide an assessment methodology and propose some evaluation metrics that could distinguish a cooperative human from a non-cooperative one. Further, we also show how geometric reasoning can be used to generate appropriate verbal responses or robot actions.

★ RoboInspector: Unveiling the Unreliability of Policy Code for LLM-enabled Robotic Manipulation

Chenduo Ying, Linkang Du, Peng Cheng, Yuanchao Shu

Large language models (LLMs) demonstrate remarkable capabilities in reasoning and code generation, enabling robotic manipulation to be initiated with just a single instruction. The LLM carries out various tasks by generating policy code required to control the robot. Despite advances in LLMs, achieving reliable policy code generation remains a significant challenge due to the diverse requirements of real-world tasks and the inherent complexity of user instructions. In practice, different users may provide distinct instructions to drive the robot for the same task, which may cause the unreliability of policy code generation. To bridge this gap, we design RoboInspector, a pipeline to unveil and characterize the unreliability of the policy code for LLM-enabled robotic manipulation from two perspectives: the complexity of the manipulation task and the granularity of the instruction. We perform comprehensive experiments with 168 distinct combinations of tasks, instructions, and LLMs in two prominent frameworks. The RoboInspector identifies four main unreliable behaviors that lead to manipulation failure. We provide a detailed characterization of these behaviors and their underlying causes, giving insight for practical development to reduce unreliability. Furthermore, we introduce a refinement approach guided by failure policy code feedback that improves the reliability of policy code generation by up to 35% in LLM-enabled robotic manipulation, evaluated in both simulation and real-world environments.

★ Dynamics-Compliant Trajectory Diffusion for Super-Nominal Payload Manipulation

Anuj Pasricha, Joewie Koh, Jay Vakil, Alessandro Roncone

Nominal payload ratings for articulated robots are typically derived from worst-case configurations, resulting in uniform payload constraints across the entire workspace. This conservative approach severely underutilizes the robot's inherent capabilities -- our analysis demonstrates that manipulators can safely handle payloads well above nominal capacity across broad regions of their workspace while staying within joint angle, velocity, acceleration, and torque limits. To address this gap between assumed and actual capability, we propose a novel trajectory generation approach using denoising diffusion models that explicitly incorporates payload constraints into the planning process. Unlike traditional sampling-based methods that rely on inefficient trial-and-error, optimization-based methods that are prohibitively slow, or kinodynamic planners that struggle with problem dimensionality, our approach generates dynamically feasible joint-space trajectories in constant time that can be directly executed on physical hardware without post-processing. Experimental validation on a 7 DoF Franka Emika Panda robot demonstrates that up to 67.6% of the workspace remains accessible even with payloads exceeding 3 times the nominal capacity. This expanded operational envelope highlights the importance of a more nuanced consideration of payload dynamics in motion planning algorithms.

comment: Accepted to 2025 Conference on Robot Learning [CoRL]

★ Multi-Modal Model Predictive Path Integral Control for Collision Avoidance

Alberto Bertipaglia, Dariu M. Gavrila, Barys Shyrokau

This paper proposes a novel approach to motion planning and decision-making for automated vehicles, using a multi-modal Model Predictive Path Integral control algorithm. The method samples with Sobol sequences around the prior input and incorporates analytical solutions for collision avoidance. By leveraging multiple modes, the multi-modal control algorithm explores diverse trajectories, such as manoeuvring around obstacles or stopping safely before them, mitigating the risk of sub-optimal solutions. A non-linear single-track vehicle model with a Fiala tyre serves as the prediction model, and tyre force constraints within the friction circle are enforced to ensure vehicle stability during evasive manoeuvres. The optimised steering angle and longitudinal acceleration are computed to generate a collision-free trajectory and to control the vehicle. In a high-fidelity simulation environment, we demonstrate that the proposed algorithm can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre on high and low-friction road surfaces and occlusion scenarios with moving obstacles, outperforming a standard Model Predictive Path Integral approach.

comment: Accepted as an oral presentation at the 29th IAVSD. August 18-22, 2025. Shanghai, China

★ Robust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty

Haojie Bai, Yang Wang, Cong Guo, Xiongwei Zhao, Hai Zhu

Achieving both safety guarantees and real-time performance in cooperative vehicle coordination remains a fundamental challenge, particularly in dynamic and uncertain environments. This paper presents a novel coordination framework that resolves this challenge through three key innovations: 1) direct control of vehicles' trajectory distributions during coordination, formulated as a robust cooperative planning problem with adaptive enhanced safety constraints, ensuring a specified level of safety regarding the uncertainty of the interactive trajectory, 2) a fully parallel ADMM-based distributed trajectory negotiation (ADMM-DTN) algorithm that efficiently solves the optimization problem while allowing configurable negotiation rounds to balance solution quality and computational resources, and 3) an interactive attention mechanism that selectively focuses on critical interactive participants to further enhance computational efficiency. Both simulation results and practical experiments demonstrate that our framework achieves significant advantages in safety (reducing collision rates by up to 40.79\% in various scenarios) and real-time performance compared to state-of-the-art methods, while maintaining strong scalability with increasing vehicle numbers. The proposed interactive attention mechanism further reduces the computational demand by 14.1\%. The framework's effectiveness is further validated through real-world experiments with unexpected dynamic obstacles, demonstrating robust coordination in complex environments. The experiment demo could be found at https://youtu.be/4PZwBnCsb6Q.

★ Cooperative Sensing Enhanced UAV Path-Following and Obstacle Avoidance with Variable Formation

Changheng Wang, Zhiqing Wei, Wangjun Jiang, Haoyue Jiang, Zhiyong Feng

The high mobility of unmanned aerial vehicles (UAVs) enables them to be used in various civilian fields, such as rescue and cargo transport. Path-following is a crucial way to perform these tasks while sensing and collision avoidance are essential for safe flight. In this paper, we investigate how to efficiently and accurately achieve path-following, obstacle sensing and avoidance subtasks, as well as their conflict-free fusion scheduling. Firstly, a high precision deep reinforcement learning (DRL)-based UAV formation path-following model is developed, and the reward function with adaptive weights is designed from the perspective of distance and velocity errors. Then, we use integrated sensing and communication (ISAC) signals to detect the obstacle and derive the Cramer-Rao lower bound (CRLB) for obstacle sensing by information-level fusion, based on which we propose the variable formation enhanced obstacle position estimation (VFEO) algorithm. In addition, an online obstacle avoidance scheme without pretraining is designed to solve the sparse reward. Finally, with the aid of null space based (NSB) behavioral method, we present a hierarchical subtasks fusion strategy. Simulation results demonstrate the effectiveness and superiority of the subtask algorithms and the hierarchical fusion strategy.

★ Observability-driven Assignment of Heterogeneous Sensors for Multi-Target Tracking IROS

Seyed Ali Rakhshan, Mehdi Golestani, He Kong

This paper addresses the challenge of assigning heterogeneous sensors (i.e., robots with varying sensing capabilities) for multi-target tracking. We classify robots into two categories: (1) sufficient sensing robots, equipped with range and bearing sensors, capable of independently tracking targets, and (2) limited sensing robots, which are equipped with only range or bearing sensors and need to at least form a pair to collaboratively track a target. Our objective is to optimize tracking quality by minimizing uncertainty in target state estimation through efficient robot-to-target assignment. By leveraging matroid theory, we propose a greedy assignment algorithm that dynamically allocates robots to targets to maximize tracking quality. The algorithm guarantees constant-factor approximation bounds of 1/3 for arbitrary tracking quality functions and 1/2 for submodular functions, while maintaining polynomial-time complexity. Extensive simulations demonstrate the algorithm's effectiveness in accurately estimating and tracking targets over extended periods. Furthermore, numerical results confirm that the algorithm's performance is close to that of the optimal assignment, highlighting its robustness and practical applicability.

comment: This paper has been accepted to the 2025 IEEE/RSJ IROS

★ Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning PRICAI25

Yibin Sun, Nick Lim, Guilherme Weigert Cassales, Heitor Murilo Gomes, Bernhard Pfahringer, Albert Bifet, Anany Dwivedi

Detecting domain shifts in myoelectric activations poses a significant challenge due to the inherent non-stationarity of electromyography (EMG) signals. This paper explores the detection of domain shifts using data stream (DS) learning techniques, focusing on the DB6 dataset from the Ninapro database. We define domains as distinct time-series segments based on different subjects and recording sessions, applying Kernel Principal Component Analysis (KPCA) with a cosine kernel to pre-process and highlight these shifts. By evaluating multiple drift detection methods such as CUSUM, Page-Hinckley, and ADWIN, we reveal the limitations of current techniques in achieving high performance for real-time domain shift detection in EMG signals. Our results underscore the potential of streaming-based approaches for maintaining stable EMG decoding models, while highlighting areas for further research to enhance robustness and accuracy in real-world scenarios.

comment: 16 pages, 5 figures, 1 table, PRICAI25

★ Learning to Assemble the Soma Cube with Legal-Action Masked DQN and Safe ZYZ Regrasp on a Doosan M0609

Jaehong Oh, Seungjun Jung, Sawoong Kim

This paper presents the first comprehensive application of legal-action masked Deep Q-Networks with safe ZYZ regrasp strategies to an underactuated gripper-equipped 6-DOF collaborative robot for autonomous Soma cube assembly learning. Our approach represents the first systematic integration of constraint-aware reinforcement learning with singularity-safe motion planning on a Doosan M0609 collaborative robot. We address critical challenges in robotic manipulation: combinatorial action space explosion, unsafe motion planning, and systematic assembly strategy learning. Our system integrates a legal-action masked DQN with hierarchical architecture that decomposes Q-function estimation into orientation and position components, reducing computational complexity from $O(3,132)$ to $O(116) + O(27)$ while maintaining solution completeness. The robot-friendly reward function encourages ground-first, vertically accessible assembly sequences aligned with manipulation constraints. Curriculum learning across three progressive difficulty levels (2-piece, 3-piece, 7-piece) achieves remarkable training efficiency: 100\% success rate for Level 1 within 500 episodes, 92.9\% for Level 2, and 39.9\% for Level 3 over 105,300 total training episodes.

comment: 13 figures, 17 pages

★ Mini Autonomous Car Driving based on 3D Convolutional Neural Networks

Pablo Moraes, Monica Rodriguez, Kristofer S. Kappel, Hiago Sodre, Santiago Fernandez, Igor Nunes, Bruna Guterres, Ricardo Grando

Autonomous driving applications have become increasingly relevant in the automotive industry due to their potential to enhance vehicle safety, efficiency, and user experience, thereby meeting the growing demand for sophisticated driving assistance features. However, the development of reliable and trustworthy autonomous systems poses challenges such as high complexity, prolonged training periods, and intrinsic levels of uncertainty. Mini Autonomous Cars (MACs) are used as a practical testbed, enabling validation of autonomous control methodologies on small-scale setups. This simplified and cost-effective environment facilitates rapid evaluation and comparison of machine learning models, which is particularly useful for algorithms requiring online training. To address these challenges, this work presents a methodology based on RGB-D information and three-dimensional convolutional neural networks (3D CNNs) for MAC autonomous driving in simulated environments. We evaluate the proposed approach against recurrent neural networks (RNNs), with architectures trained and tested on two simulated tracks with distinct environmental features. Performance was assessed using task completion success, lap-time metrics, and driving consistency. Results highlight how architectural modifications and track complexity influence the models' generalization capability and vehicle control performance. The proposed 3D CNN demonstrated promising results when compared with RNNs.

♻ ★ UltraTac: Integrated Ultrasound-Augmented Visuotactile Sensor for Enhanced Robotic Perception IROS 2025

Junhao Gong, Kit-Wa Sou, Shoujie Li, Changqing Guo, Yan Huang, Chuqiao Lyu, Ziwu Song, Wenbo Ding

Visuotactile sensors provide high-resolution tactile information but are incapable of perceiving the material features of objects. We present UltraTac, an integrated sensor that combines visuotactile imaging with ultrasound sensing through a coaxial optoacoustic architecture. The design shares structural components and achieves consistent sensing regions for both modalities. Additionally, we incorporate acoustic matching into the traditional visuotactile sensor structure, enabling integration of the ultrasound sensing modality without compromising visuotactile performance. Through tactile feedback, we dynamically adjust the operating state of the ultrasound module to achieve flexible functional coordination. Systematic experiments demonstrate three key capabilities: proximity sensing in the 3-8 cm range ($R^2=0.90$), material classification (average accuracy: 99.20%), and texture-material dual-mode object recognition achieving 92.11% accuracy on a 15-class task. Finally, we integrate the sensor into a robotic manipulation system to concurrently detect container surface patterns and internal content, which verifies its potential for advanced human-machine interaction and precise robotic manipulation.

comment: Accepted to IROS 2025

♻ ★ COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty

Ricardo Cannizzaro, Michael Groom, Jonathan Routley, Robert Osazuwa Ness, Lars Kunze

Manipulation tasks require robots to reason about cause and effect when interacting with objects. Yet, many data-driven approaches lack causal semantics and thus only consider correlations. We introduce COBRA-PPM, a novel causal Bayesian reasoning architecture that combines causal Bayesian networks and probabilistic programming to perform interventional inference for robot manipulation under uncertainty. We demonstrate its capabilities through high-fidelity Gazebo-based experiments on an exemplar block stacking task, where it predicts manipulation outcomes with high accuracy (Pred Acc: 88.6%) and performs greedy next-best action selection with a 94.2% task success rate. We further demonstrate sim2real transfer on a domestic robot, showing effectiveness in handling real-world uncertainty from sensor noise and stochastic actions. Our generalised and extensible framework supports a wide range of manipulation scenarios and lays a foundation for future work at the intersection of robotics and causality.

comment: 8 pages, 7 figures, accepted to the 2025 IEEE European Conference on Mobile Robots (ECMR 2025)

♻ ★ Centralization vs. decentralization in multi-robot sweep coverage with ground robots and UAVs

Aryo Jamshidpey, Mostafa Wahby, Michael Allwright, Weixu Zhu, Marco Dorigo, Mary Katherine Heinrich

In swarm robotics, decentralized control is often proposed as a more scalable and fault-tolerant alternative to centralized control. However, centralized behaviors are often faster and more efficient than their decentralized counterparts. In any given application, the goals and constraints of the task being solved should guide the choice to use centralized control, decentralized control, or a combination of the two. Currently, the exact trade-offs that exist between centralization and decentralization are not well defined. In this paper, we compare the performance of centralization and decentralization in the example task of sweep coverage, across five different types of multi-robot control structures: random walk, decentralized with beacons, hybrid formation control using self-organizing hierarchy, centralized formation control, and predetermined. In all five approaches, the coverage task is completed by a group of ground robots. In each approach, except for the random walk, the ground robots are assisted by UAVs, acting as supervisors or beacons. We compare the approaches in terms of three performance metrics for which centralized approaches are expected to have an advantage -- coverage completeness, coverage uniformity, and sweep completion time -- and two metrics for which decentralized approaches are expected to have an advantage -- scalability (4, 8, or 16 ground robots) and fault tolerance (0%, 25%, 50%, or 75% ground robot failure).

comment: IRIDIA, Universite Libre de Bruxelles, Brussels, Belgium, 2021

♻ ★ Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation

Abdulaziz Almuzairee, Rohan Patil, Dwait Bhatt, Henrik I. Christensen

Vision is well-known for its use in manipulation, especially using visual servoing. Due to the 3D nature of the world, using multiple camera views and merging them creates better representations for Q-learning and in turn, trains more sample efficient policies. Nevertheless, these multi-view policies are sensitive to failing cameras and can be burdensome to deploy. To mitigate these issues, we introduce a Merge And Disentanglement (MAD) algorithm that efficiently merges views to increase sample efficiency while simultaneously disentangling views by augmenting multi-view feature inputs with single-view features. This produces robust policies and allows lightweight deployment. We demonstrate the efficiency and robustness of our approach using Meta-World and ManiSkill3. For project website and code, see https://aalmuzairee.github.io/mad

comment: Accepted at CoRL 2025. For project website and code, see https://aalmuzairee.github.io/mad

♻ ★ Ontology Neural Network and ORTSF: A Framework for Topological Reasoning and Delay-Robust Control

Jaehong Oh

The advancement of autonomous robotic systems has led to impressive capabilities in perception, localization, mapping, and control. Yet, a fundamental gap remains: existing frameworks excel at geometric reasoning and dynamic stability but fall short in representing and preserving relational semantics, contextual reasoning, and cognitive transparency essential for collaboration in dynamic, human-centric environments. This paper introduces a unified architecture comprising the Ontology Neural Network (ONN) and the Ontological Real-Time Semantic Fabric (ORTSF) to address this gap. The ONN formalizes relational semantic reasoning as a dynamic topological process. By embedding Forman-Ricci curvature, persistent homology, and semantic tensor structures within a unified loss formulation, ONN ensures that relational integrity and topological coherence are preserved as scenes evolve over time. The ORTSF transforms reasoning traces into actionable control commands while compensating for system delays. It integrates predictive and delay-aware operators that ensure phase margin preservation and continuity of control signals, even under significant latency conditions. Empirical studies demonstrate the ONN + ORTSF framework's ability to unify semantic cognition and robust control, providing a mathematically principled and practically viable solution for cognitive robotics.

comment: 12 pages, 5 figures, includes theoretical proofs and simulation results

♻ ★ Knowledge in multi-robot systems: an interplay of dynamics, computation and communication

Giorgio Cignarale, Stephan Felber, Eric Goubault, Bernardo Hummes Flores, Hugo Rincon Galeana

In this paper, we provide a framework integrating distributed multi-robot systems and temporal epistemic logic. We show that continuous-discrete hybrid systems are compatible with logical models of knowledge already used in distributed computing, and demonstrate its usefulness by deriving sufficient epistemic conditions for exploration and gathering robot tasks to be solvable. We provide a separation of the physical and computational aspects of a robotic system, allowing us to decouple the problems related to each and directly use methods from control theory and distributed computing, fields that are traditionally distant in the literature. Finally, we demonstrate a novel approach for reasoning about the knowledge in multi-robot systems through a principled method of converting a switched hybrid dynamical system into a temporal-epistemic logic model, passing through an abstract state machine representation. This creates space for methods and results to be exchanged across the fields of control theory, distributed computing and temporal-epistemic logic, while reasoning about multi-robot systems.

♻ ★ CoRI: Communication of Robot Intent for Physical Human-Robot Interaction

Junxiang Wang, Emek Barış Küçüktabak, Rana Soltani Zarrin, Zackory Erickson

Clear communication of robot intent fosters transparency and interpretability in physical human-robot interaction (pHRI), particularly during assistive tasks involving direct human-robot contact. We introduce CoRI, a pipeline that automatically generates natural language communication of a robot's upcoming actions directly from its motion plan and visual perception. Our pipeline first processes the robot's image view to identify human poses and key environmental features. It then encodes the planned 3D spatial trajectory (including velocity and force) onto this view, visually grounding the path and its dynamics. CoRI queries a vision-language model with this visual representation to interpret the planned action within the visual context before generating concise, user-directed statements, without relying on task-specific information. Results from a user study involving robot-assisted feeding, bathing, and shaving tasks across two different robots indicate that CoRI leads to statistically significant difference in communication clarity compared to a baseline communication strategy. Specifically, CoRI effectively conveys not only the robot's high-level intentions but also crucial details about its motion and any collaborative user action needed. Video and code of our project can be found on our project website: https://cori-phri.github.io/

comment: To be published in Proceedings of the 9th Conference on Robot Learning (CoRL). 34 pages, 10 figures

♻ ★ Traversing the Narrow Path: A Two-Stage Reinforcement Learning Framework for Humanoid Beam Walking

TianChen Huang, Wei Gao, Runchen Xu, Shiwu Zhang

Traversing narrow beams is challenging for humanoids due to sparse, safety-critical contacts and the fragility of purely learned policies. We propose a physically grounded, two-stage framework that couples an XCoM/LIPM footstep template with a lightweight residual planner and a simple low-level tracker. Stage-1 is trained on flat ground: the tracker learns to robustly follow footstep targets by adding small random perturbations to heuristic footsteps, without any hand-crafted centerline locking, so it acquires stable contact scheduling and strong target-tracking robustness. Stage-2 is trained in simulation on a beam: a high-level planner predicts a body-frame residual (Delta x, Delta y, Delta psi) for the swing foot only, refining the template step to prioritize safe, precise placement under narrow support while preserving interpretability. To ease deployment, sensing is kept minimal and consistent between simulation and hardware: the planner consumes compact, forward-facing elevation cues together with onboard IMU and joint signals. On a Unitree G1, our system reliably traverses a 0.2 m-wide, 3 m-long beam. Across simulation and real-world studies, residual refinement consistently outperforms template-only and monolithic baselines in success rate, centerline adherence, and safety margins, while the structured footstep interface enables transparent analysis and low-friction sim-to-real transfer.

comment: Project website: https://huangtc233.github.io/Traversing-the-Narrow-Path/

♻ ★ Soft Manipulation Surface With Reduced Actuator Density For Heterogeneous Object Manipulation

Pratik Ingle, Kasper Støy, Andres Faiña

Object manipulation in robotics faces challenges due to diverse object shapes, sizes, and fragility. Gripper-based methods offer precision and low degrees of freedom (DOF) but the gripper limits the kind of objects to grasp. On the other hand, surface-based approaches provide flexibility for handling fragile and heterogeneous objects but require numerous actuators, increasing complexity. We propose new manipulation hardware that utilizes equally spaced linear actuators placed vertically and connected by a soft surface. In this setup, object manipulation occurs on the soft surface through coordinated movements of the surrounding actuators. This approach requires fewer actuators to cover a large manipulation area, offering a cost-effective solution with a lower DOF compared to dense actuator arrays. It also effectively handles heterogeneous objects of varying shapes and weights, even when they are significantly smaller than the distance between actuators. This method is particularly suitable for managing highly fragile objects in the food industry.

♻ ★ Unified Path Planner with Adaptive Safety and Optimality

Jatin Kumar Arora, Soutrik Bandyopadhyay, Shubhendu Bhasin

Path planning for autonomous robots presents a fundamental trade-off between optimality and safety. While conventional algorithms typically prioritize one of these objectives, we introduce the Unified Path Planner (UPP), a unified framework that simultaneously addresses both. UPP is a graph-search-based algorithm that employs a modified heuristic function incorporating a dynamic safety cost, enabling an adaptive balance between path length and obstacle clearance. We establish theoretical sub-optimality bounds for the planner and demonstrate that its safety-to-optimality ratio can be tuned via adjustable parameters, with a trade-off in computational complexity. Extensive simulations show that UPP achieves a high success rate, generating near-optimal paths with only a negligible increase in cost over traditional A*, while ensuring safety margins that closely approach those of the classical Voronoi planner. Finally, the practical efficacy of UPP is validated through a hardware implementation on a TurtleBot, confirming its ability to navigate cluttered environments by generating safe, sub-optimal paths.

comment: 6 pages,4 figures

♻ ★ Latent Adaptive Planner for Dynamic Manipulation

Donghun Noh, Deqian Kong, Minglu Zhao, Andrew Lizarraga, Jianwen Xie, Ying Nian Wu, Dennis Hong

We present the Latent Adaptive Planner (LAP), a trajectory-level latent-variable policy for dynamic nonprehensile manipulation (e.g., box catching) that formulates planning as inference in a low-dimensional latent space and is learned effectively from human demonstration videos. During execution, LAP achieves real-time adaptation by maintaining a posterior over the latent plan and performing variational replanning as new observations arrive. To bridge the embodiment gap between humans and robots, we introduce a model-based proportional mapping that regenerates accurate kinematic-dynamic joint states and object positions from human demonstrations. Through challenging box catching experiments with varying object properties, LAP demonstrates superior success rates, trajectory smoothness, and energy efficiency by learning human-like compliant motions and adaptive behaviors. Overall, LAP enables dynamic manipulation with real-time adaptation and successfully transfer across heterogeneous robot platforms using the same human demonstration videos.

♻ ★ SignLoc: Robust Localization using Navigation Signs and Public Maps

Nicky Zimmerman, Joel Loo, Ayush Agrawal, David Hsu

Navigation signs and maps, such as floor plans and street maps, are widely available and serve as ubiquitous aids for way-finding in human environments. Yet, they are rarely used by robot systems. This paper presents SignLoc, a global localization method that leverages navigation signs to localize the robot on publicly available maps -- specifically floor plans and OpenStreetMap (OSM) graphs -- without prior sensor-based mapping. SignLoc first extracts a navigation graph from the input map. It then employs a probabilistic observation model to match directional and locational cues from the detected signs to the graph, enabling robust topo-semantic localization within a Monte Carlo framework. We evaluated SignLoc in diverse large-scale environments: part of a university campus, a shopping mall, and a hospital complex. Experimental results show that SignLoc reliably localizes the robot after observing only one to two signs.

comment: This work has been submitted to the IEEE for possible publication

♻ ★ Visual Imitation Enables Contextual Humanoid Control

Arthur Allshire, Hongsuk Choi, Junyi Zhang, David McAllister, Anthony Zhang, Chung Min Kim, Trevor Darrell, Pieter Abbeel, Jitendra Malik, Angjoo Kanazawa

How can we teach humanoids to climb staircases and sit on chairs using the surrounding environment context? Arguably, the simplest way is to just show them-casually capture a human motion video and feed it to humanoids. We introduce VIDEOMIMIC, a real-to-sim-to-real pipeline that mines everyday videos, jointly reconstructs the humans and the environment, and produces whole-body control policies for humanoid robots that perform the corresponding skills. We demonstrate the results of our pipeline on real humanoid robots, showing robust, repeatable contextual control such as staircase ascents and descents, sitting and standing from chairs and benches, as well as other dynamic whole-body skills-all from a single policy, conditioned on the environment and global root commands. VIDEOMIMIC offers a scalable path towards teaching humanoids to operate in diverse real-world environments.

comment: Project website: https://www.videomimic.net/

♻ ★ QuaDreamer: Controllable Panoramic Video Generation for Quadruped Robots

Sheng Wu, Fei Teng, Hao Shi, Qi Jiang, Kai Luo, Kaiwei Wang, Kailun Yang

Panoramic cameras, capturing comprehensive 360-degree environmental data, are suitable for quadruped robots in surrounding perception and interaction with complex environments. However, the scarcity of high-quality panoramic training data-caused by inherent kinematic constraints and complex sensor calibration challenges-fundamentally limits the development of robust perception systems tailored to these embodied platforms. To address this issue, we propose QuaDreamer-the first panoramic data generation engine specifically designed for quadruped robots. QuaDreamer focuses on mimicking the motion paradigm of quadruped robots to generate highly controllable, realistic panoramic videos, providing a data source for downstream tasks. Specifically, to effectively capture the unique vertical vibration characteristics exhibited during quadruped locomotion, we introduce Vertical Jitter Encoding (VJE). VJE extracts controllable vertical signals through frequency-domain feature filtering and provides high-quality prompts. To facilitate high-quality panoramic video generation under jitter signal control, we propose a Scene-Object Controller (SOC) that effectively manages object motion and boosts background jitter control through the attention mechanism. To address panoramic distortions in wide-FoV video generation, we propose the Panoramic Enhancer (PE)-a dual-stream architecture that synergizes frequency-texture refinement for local detail enhancement with spatial-structure correction for global geometric consistency. We further demonstrate that the generated video sequences can serve as training data for the quadruped robot's panoramic visual perception model, enhancing the performance of multi-object tracking in 360-degree scenes. The source code and model weights will be publicly available at https://github.com/losehu/QuaDreamer.

comment: Accepted to CoRL 2025. The source code and model weights will be publicly available at \url{https://github.com/losehu/QuaDreamer

♻ ★ Towards Embodiment Scaling Laws in Robot Locomotion

Bo Ai, Liu Dai, Nico Bohlinger, Dichen Li, Tongzhou Mu, Zhanxin Wu, K. Fay, Henrik I. Christensen, Jan Peters, Hao Su

Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodiment scaling laws, the hypothesis that increasing the number of training embodiments improves generalization to unseen ones, using robot locomotion as a test bed. We procedurally generate ~1,000 embodiments with topological, geometric, and joint-level kinematic variations, and train policies on random subsets. We observe positive scaling trends supporting the hypothesis, and find that embodiment scaling enables substantially broader generalization than data scaling on fixed embodiments. Our best policy, trained on the full dataset, transfers zero-shot to novel embodiments in simulation and the real world, including the Unitree Go2 and H1. These results represent a step toward general embodied intelligence, with relevance to adaptive control for configurable robots, morphology co-design, and beyond.

comment: Conference on Robot Learning (CoRL), 2025. Project website: https://embodiment-scaling-laws.github.io/

♻ ★ Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility

Zewei Zhang, Chenhao Li, Takahiro Miki, Marco Hutter

Reinforcement learning (RL)-based motion imitation methods trained on demonstration data can effectively learn natural and expressive motions with minimal reward engineering but often struggle to generalize to novel environments. We address this by proposing a hierarchical RL framework in which a low-level policy is first pre-trained to imitate animal motions on flat ground, thereby establishing motion priors. A subsequent high-level, goal-conditioned policy then builds on these priors, learning residual corrections that enable perceptive locomotion, local obstacle avoidance, and goal-directed navigation across diverse and rugged terrains. Simulation experiments illustrate the effectiveness of learned residuals in adapting to progressively challenging uneven terrains while still preserving the locomotion characteristics provided by the motion priors. Furthermore, our results demonstrate improvements in motion regularization over baseline models trained without motion priors under similar reward setups. Real-world experiments with an ANYmal-D quadruped robot confirm our policy's capability to generalize animal-like locomotion skills to complex terrains, demonstrating smooth and efficient locomotion and local navigation performance amidst challenging terrains with obstacles.

comment: Conference on Robot Learning (CoRL)

♻ ★ Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation

Tongxuan Tian, Haoyang Li, Bo Ai, Xiaodi Yuan, Zhiao Huang, Hao Su

Cloth manipulation is challenging due to its highly complex dynamics, near-infinite degrees of freedom, and frequent self-occlusions, which complicate both state estimation and dynamics modeling. Inspired by recent advances in generative models, we hypothesize that these expressive models can effectively capture intricate cloth configurations and deformation patterns from data. Therefore, we propose a diffusion-based generative approach for both perception and dynamics modeling. Specifically, we formulate state estimation as reconstructing full cloth states from partial observations and dynamics modeling as predicting future states given the current state and robot actions. Leveraging a transformer-based diffusion model, our method achieves accurate state reconstruction and reduces long-horizon dynamics prediction errors by an order of magnitude compared to prior approaches. We integrate our dynamics models with model predictive control and show that our framework enables effective cloth folding on real robotic systems, demonstrating the potential of generative models for deformable object manipulation under partial observability and complex dynamics.

comment: CoRL 2025. Project website: https://uniclothdiff.github.io/

♻ ★ DexFruit: Dexterous Manipulation and Gaussian Splatting Inspection of Fruit

Aiden Swann, Alex Qiu, Matthew Strong, Angelina Zhang, Samuel Morstein, Kai Rayle, Monroe Kennedy III

DexFruit is a robotic manipulation framework that enables gentle, autonomous handling of fragile fruit and precise evaluation of damage. Many fruits are fragile and prone to bruising, thus requiring humans to manually harvest them with care. In this work, we demonstrate by using optical tactile sensing, autonomous manipulation of fruit with minimal damage can be achieved. We show that our tactile informed diffusion policies outperform baselines in both reduced bruising and pick-and-place success rate across three fruits: strawberries, tomatoes, and blackberries. In addition, we introduce FruitSplat, a novel technique to represent and quantify visual damage in high-resolution 3D representation via 3D Gaussian Splatting (3DGS). Existing metrics for measuring damage lack quantitative rigor or require expensive equipment. With FruitSplat, we distill a 2D strawberry mask as well as a 2D bruise segmentation mask into the 3DGS representation. Furthermore, this representation is modular and general, compatible with any relevant 2D model. Overall, we demonstrate a 92% grasping policy success rate, up to a 20% reduction in visual bruising, and up to an 31% improvement in grasp success rate on challenging fruit compared to our baselines across our three tested fruits. We rigorously evaluate this result with over 630 trials. Please checkout our website at https://dex-fruit.github.io .

comment: 8 pages, 5 figures

♻ ★ Energy-Aware Lane Planning for Connected Electric Vehicles in Urban Traffic: Design and Vehicle-in-the-Loop Validation

Hansung Kim, Eric Yongkeun Choi, Eunhyek Joa, Hotae Lee, Linda Lim, Scott Moura, Francesco Borrelli

Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. This neglects the significant impact of lateral decisions, such as lane changes, on overall energy efficiency, especially in environments with traffic signals and heterogeneous traffic flow. To address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our approach estimates long-term energy costs using a graph-based approximation and solves short-horizon optimal control problems under traffic constraints. Using a data-driven energy model calibrated to an actual battery electric vehicle, we demonstrate with vehicle-in-the-loop experiments that our method reduces motion energy consumption by up to 24 percent compared to a human driver, highlighting the potential of connectivity-enabled planning for sustainable urban autonomy.

comment: Accepted at 2025 IEEE Conference on Decision and Control (CDC25')

♻ ★ A Physics-Based Continuum Model for Versatile, Scalable, and Fast Terramechanics Simulation

Huzaifa Unjhawala, Luning Bakke, Harry Zhang, Michael Taylor, Ganesh Arivoli, Radu Serban, Dan Negrut

This paper discusses Chrono's Continuous Representation Model (called herein Chrono::CRM), a general-purpose, scalable, and efficient simulation solution for terramechanics problems. Built on Chrono's Smoothed Particle Hydrodynamics (SPH) framework, Chrono::CRM moves beyond semi-empirical terramechanics approaches, e.g., Bekker-Wong/Janosi-Hanamoto, to provide a physics-based model able to address complex tasks such as digging, grading, as well as interaction with deformable wheels and complex grouser/lug patterns. The terramechanics model is versatile in that it allows the terrain to interact with both rigid and flexible implements simulated via the Chrono dynamics engine. We validate Chrono::CRM against experimental data from three physical tests, including one involving NASA's MGRU3 rover. In addition, the simulator is benchmarked against a high-fidelity Discrete Element Method (DEM) simulation of a digging scenario involving the Regolith Advanced Surface Systems Operations Robot (RASSOR). Being GPU-accelerated, Chrono::CRM achieves computational efficiency comparable to that of semi-empirical simulation approaches for terramechanics problems. Through an ``active domains'' implementation, Chrono::CRM can handle terrain stretches up to 10 km long with 100 million SPH particles at near interactive rates, making high-fidelity off-road simulations at large scales feasible. As a component of the Chrono package, the CRM model is open source and released under a BSD-3 license. All models and simulations used in this contribution are available in a public GitHub repository for reproducibility studies and further research.

comment: 32 pages, 21 figures, Submitted to Journal of Terramechanics