Robotics
★ Whole-Body Conditioned Egocentric Video Prediction
We train models to Predict Ego-centric Video from human Actions (PEVA), given
the past video and an action represented by the relative 3D body pose. By
conditioning on kinematic pose trajectories, structured by the joint hierarchy
of the body, our model learns to simulate how physical human actions shape the
environment from a first-person point of view. We train an auto-regressive
conditional diffusion transformer on Nymeria, a large-scale dataset of
real-world egocentric video and body pose capture. We further design a
hierarchical evaluation protocol with increasingly challenging tasks, enabling
a comprehensive analysis of the model's embodied prediction and control
abilities. Our work represents an initial attempt to tackle the challenges of
modeling complex real-world environments and embodied agent behaviors with
video prediction from the perspective of a human.
comment: Project Page: https://dannytran123.github.io/PEVA
★ SAM4D: Segment Anything in Camera and LiDAR Streams ICCV2025
We present SAM4D, a multi-modal and temporal foundation model designed for
promptable segmentation across camera and LiDAR streams. Unified Multi-modal
Positional Encoding (UMPE) is introduced to align camera and LiDAR features in
a shared 3D space, enabling seamless cross-modal prompting and interaction.
Additionally, we propose Motion-aware Cross-modal Memory Attention (MCMA),
which leverages ego-motion compensation to enhance temporal consistency and
long-horizon feature retrieval, ensuring robust segmentation across dynamically
changing autonomous driving scenes. To avoid annotation bottlenecks, we develop
a multi-modal automated data engine that synergizes VFM-driven video masklets,
spatiotemporal 4D reconstruction, and cross-modal masklet fusion. This
framework generates camera-LiDAR aligned pseudo-labels at a speed orders of
magnitude faster than human annotation while preserving VFM-derived semantic
fidelity in point cloud representations. We conduct extensive experiments on
the constructed Waymo-4DSeg, which demonstrate the powerful cross-modal
segmentation ability and great potential in data annotation of proposed SAM4D.
comment: Accepted by ICCV2025, Project Page: https://SAM4D-Project.github.io
★ WorldVLA: Towards Autoregressive Action World Model
Jun Cen, Chaohui Yu, Hangjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, Deli Zhao, Hao Chen
We present WorldVLA, an autoregressive action world model that unifies action
and image understanding and generation. Our WorldVLA intergrates
Vision-Language-Action (VLA) model and world model in one single framework. The
world model predicts future images by leveraging both action and image
understanding, with the purpose of learning the underlying physics of the
environment to improve action generation. Meanwhile, the action model generates
the subsequent actions based on image observations, aiding in visual
understanding and in turn helps visual generation of the world model. We
demonstrate that WorldVLA outperforms standalone action and world models,
highlighting the mutual enhancement between the world model and the action
model. In addition, we find that the performance of the action model
deteriorates when generating sequences of actions in an autoregressive manner.
This phenomenon can be attributed to the model's limited generalization
capability for action prediction, leading to the propagation of errors from
earlier actions to subsequent ones. To address this issue, we propose an
attention mask strategy that selectively masks prior actions during the
generation of the current action, which shows significant performance
improvement in the action chunk generation task.
comment: Code: https://github.com/alibaba-damo-academy/WorldVLA
★ Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning
Generative models such as diffusion and flow-matching offer expressive
policies for offline reinforcement learning (RL) by capturing rich, multimodal
action distributions, but their iterative sampling introduces high inference
costs and training instability due to gradient propagation across sampling
steps. We propose the \textit{Single-Step Completion Policy} (SSCP), a
generative policy trained with an augmented flow-matching objective to predict
direct completion vectors from intermediate flow samples, enabling accurate,
one-shot action generation. In an off-policy actor-critic framework, SSCP
combines the expressiveness of generative models with the training and
inference efficiency of unimodal policies, without requiring long
backpropagation chains. Our method scales effectively to offline,
offline-to-online, and online RL settings, offering substantial gains in speed
and adaptability over diffusion-based baselines. We further extend SSCP to
goal-conditioned RL, enabling flat policies to exploit subgoal structures
without explicit hierarchical inference. SSCP achieves strong results across
standard offline RL and behavior cloning benchmarks, positioning it as a
versatile, expressive, and efficient framework for deep RL and sequential
decision-making.
★ EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting
Efficient three-dimensional reconstruction and real-time visualization are
critical in surgical scenarios such as endoscopy. In recent years, 3D Gaussian
Splatting (3DGS) has demonstrated remarkable performance in efficient 3D
reconstruction and rendering. Most 3DGS-based Simultaneous Localization and
Mapping (SLAM) methods only rely on the appearance constraints for optimizing
both 3DGS and camera poses. However, in endoscopic scenarios, the challenges
include photometric inconsistencies caused by non-Lambertian surfaces and
dynamic motion from breathing affects the performance of SLAM systems. To
address these issues, we additionally introduce optical flow loss as a
geometric constraint, which effectively constrains both the 3D structure of the
scene and the camera motion. Furthermore, we propose a depth regularisation
strategy to mitigate the problem of photometric inconsistencies and ensure the
validity of 3DGS depth rendering in endoscopic scenes. In addition, to improve
scene representation in the SLAM system, we improve the 3DGS refinement
strategy by focusing on viewpoints corresponding to Keyframes with suboptimal
rendering quality frames, achieving better rendering results. Extensive
experiments on the C3VD static dataset and the StereoMIS dynamic dataset
demonstrate that our method outperforms existing state-of-the-art methods in
novel view synthesis and pose estimation, exhibiting high performance in both
static and dynamic surgical scenes. The source code will be publicly available
upon paper acceptance.
★ ToosiCubix: Monocular 3D Cuboid Labeling via Vehicle Part Annotations
Many existing methods for 3D cuboid annotation of vehicles rely on expensive
and carefully calibrated camera-LiDAR or stereo setups, limiting their
accessibility for large-scale data collection. We introduce ToosiCubix, a
simple yet powerful approach for annotating ground-truth cuboids using only
monocular images and intrinsic camera parameters. Our method requires only
about 10 user clicks per vehicle, making it highly practical for adding 3D
annotations to existing datasets originally collected without specialized
equipment. By annotating specific features (e.g., wheels, car badge,
symmetries) across different vehicle parts, we accurately estimate each
vehicle's position, orientation, and dimensions up to a scale ambiguity (8
DoF). The geometric constraints are formulated as an optimization problem,
which we solve using a coordinate descent strategy, alternating between
Perspective-n-Points (PnP) and least-squares subproblems. To handle common
ambiguities such as scale and unobserved dimensions, we incorporate
probabilistic size priors, enabling 9 DoF cuboid placements. We validate our
annotations against the KITTI and Cityscapes3D datasets, demonstrating that our
method offers a cost-effective and scalable solution for high-quality 3D cuboid
annotation.
★ Real-time Terrain Analysis for Off-road Autonomous Vehicles
This research addresses critical autonomous vehicle control challenges
arising from road roughness variation, which induces course deviations and
potential loss of road contact during steering operations. We present a novel
real-time road roughness estimation system employing Bayesian calibration
methodology that processes axle accelerations to predict terrain roughness with
quantifiable confidence measures. The technical framework integrates a Gaussian
process surrogate model with a simulated half-vehicle model, systematically
processing vehicle velocity and road surface roughness parameters to generate
corresponding axle acceleration responses. The Bayesian calibration routine
performs inverse estimation of road roughness from observed accelerations and
velocities, yielding posterior distributions that quantify prediction
uncertainty for adaptive risk management. Training data generation utilizes
Latin Hypercube sampling across comprehensive velocity and roughness parameter
spaces, while the calibrated model integrates seamlessly with a Simplex
controller architecture to dynamically adjust velocity limits based on
real-time roughness predictions. Experimental validation on stochastically
generated surfaces featuring varying roughness regions demonstrates robust
real-time characterization capabilities, with the integrated Simplex control
strategy effectively enhancing autonomous vehicle operational safety through
proactive surface condition response. This innovative Bayesian framework
establishes a comprehensive foundation for mitigating roughness-related
operational risks while simultaneously improving efficiency and safety margins
in autonomous vehicle systems.
★ "Who Should I Believe?": User Interpretation and Decision-Making When a Family Healthcare Robot Contradicts Human Memory
Advancements in robotic capabilities for providing physical assistance,
psychological support, and daily health management are making the deployment of
intelligent healthcare robots in home environments increasingly feasible in the
near future. However, challenges arise when the information provided by these
robots contradicts users' memory, raising concerns about user trust and
decision-making. This paper presents a study that examines how varying a
robot's level of transparency and sociability influences user interpretation,
decision-making and perceived trust when faced with conflicting information
from a robot. In a 2 x 2 between-subjects online study, 176 participants
watched videos of a Furhat robot acting as a family healthcare assistant and
suggesting a fictional user to take medication at a different time from that
remembered by the user. Results indicate that robot transparency influenced
users' interpretation of information discrepancies: with a low transparency
robot, the most frequent assumption was that the user had not correctly
remembered the time, while with the high transparency robot, participants were
more likely to attribute the discrepancy to external factors, such as a partner
or another household member modifying the robot's information. Additionally,
participants exhibited a tendency toward overtrust, often prioritizing the
robot's recommendations over the user's memory, even when suspecting system
malfunctions or third-party interference. These findings highlight the impact
of transparency mechanisms in robotic systems, the complexity and importance
associated with system access control for multi-user robots deployed in home
environments, and the potential risks of users' over reliance on robots in
sensitive domains such as healthcare.
comment: 8 pages
★ Active Disturbance Rejection Control for Trajectory Tracking of a Seagoing USV: Design, Simulation, and Field Experiments IROS 2025
Unmanned Surface Vessels (USVs) face significant control challenges due to
uncertain environmental disturbances like waves and currents. This paper
proposes a trajectory tracking controller based on Active Disturbance Rejection
Control (ADRC) implemented on the DUS V2500. A custom simulation incorporating
realistic waves and current disturbances is developed to validate the
controller's performance, supported by further validation through field tests
in the harbour of Scheveningen, the Netherlands, and at sea. Simulation results
demonstrate that ADRC significantly reduces cross-track error across all tested
conditions compared to a baseline PID controller but increases control effort
and energy consumption. Field trials confirm these findings while revealing a
further increase in energy consumption during sea trials compared to the
baseline.
comment: Accepted for presentation at IROS 2025. Submitted version
★ ACTLLM: Action Consistency Tuned Large Language Model
This paper introduces ACTLLM (Action Consistency Tuned Large Language Model),
a novel approach for robot manipulation in dynamic environments. Traditional
vision-based systems often struggle to learn visual representations that excel
in both task execution and spatial reasoning, thereby limiting their
adaptability in dynamic environments. ACTLLM addresses these challenges by
harnessing language to craft structured scene descriptors, providing a uniform
interface for both spatial understanding and task performance through flexible
language instructions. Moreover, we introduce a novel action consistency
constraint that aligns visual perception with corresponding actions, thereby
enhancing the learning of actionable visual representations. Additionally, we
have reformulated the Markov decision process for manipulation tasks into a
multi-turn visual dialogue framework. This approach enables the modeling of
long-term task execution with enhanced contextual relevance derived from the
history of task execution. During our evaluation, ACTLLM excels in diverse
scenarios, proving its effectiveness on challenging vision-based robot
manipulation tasks.
★ Real-Time ESFP: Estimating, Smoothing, Filtering, and Pose-Mapping
This paper presents ESFP, an end-to-end pipeline that converts monocular RGB
video into executable joint trajectories for a low-cost 4-DoF desktop arm. ESFP
comprises four sequential modules. (1) Estimating: ROMP lifts each frame to a
24-joint 3-D skeleton. (2) Smoothing: the proposed HPSTM-a sequence-to-sequence
Transformer with self-attention-combines long-range temporal context with a
differentiable forward-kinematics decoder, enforcing constant bone lengths and
anatomical plausibility while jointly predicting joint means and full
covariances. (3) Filtering: root-normalized trajectories are variance-weighted
according to HPSTM's uncertainty estimates, suppressing residual noise. (4)
Pose-Mapping: a geometric retargeting layer transforms shoulder-elbow-wrist
triples into the uArm's polar workspace, preserving wrist orientation.
★ World-aware Planning Narratives Enhance Large Vision-Language Model Planner
Large Vision-Language Models (LVLMs) show promise for embodied planning tasks
but struggle with complex scenarios involving unfamiliar environments and
multi-step goals. Current approaches rely on environment-agnostic imitation
learning that disconnects instructions from environmental contexts, causing
models to struggle with context-sensitive instructions and rely on
supplementary cues rather than visual reasoning during long-horizon
interactions. In this work, we propose World-Aware Planning Narrative
Enhancement (WAP), a framework that infuses LVLMs with comprehensive
environmental understanding through four cognitive capabilities (visual
appearance modeling, spatial reasoning, functional abstraction, and syntactic
grounding) while developing and evaluating models using only raw visual
observations through curriculum learning. Evaluations on the EB-ALFRED
benchmark demonstrate substantial improvements, with Qwen2.5-VL achieving a
60.7 absolute improvement in task success rates, particularly in commonsense
reasoning (+60.0) and long-horizon planning (+70.0). Notably, our enhanced
open-source models outperform proprietary systems like GPT-4o and
Claude-3.5-Sonnet by a large margin.
★ Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations IROS 2025
Deploying mobile robots safely among humans requires the motion planner to
account for the uncertainty in the other agents' predicted trajectories. This
remains challenging in traditional approaches, especially with arbitrarily
shaped predictions and real-time constraints. To address these challenges, we
propose a Dynamic Risk-Aware Model Predictive Path Integral control (DRA-MPPI),
a motion planner that incorporates uncertain future motions modelled with
potentially non-Gaussian stochastic predictions. By leveraging MPPI's
gradient-free nature, we propose a method that efficiently approximates the
joint Collision Probability (CP) among multiple dynamic obstacles for several
hundred sampled trajectories in real-time via a Monte Carlo (MC) approach. This
enables the rejection of samples exceeding a predefined CP threshold or the
integration of CP as a weighted objective within the navigation cost function.
Consequently, DRA-MPPI mitigates the freezing robot problem while enhancing
safety. Real-world and simulated experiments with multiple dynamic obstacles
demonstrate DRA-MPPI's superior performance compared to state-of-the-art
approaches, including Scenario-based Model Predictive Control (S-MPC), Frenet
planner, and vanilla MPPI.
comment: Accepted for presentation at IROS 2025. Submitted Version
★ Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation ICCV 2025
Panoramic image processing is essential for omni-context perception, yet
faces constraints like distortions, perspective occlusions, and limited
annotations. Previous unsupervised domain adaptation methods transfer knowledge
from labeled pinhole data to unlabeled panoramic images, but they require
access to source pinhole data. To address these, we introduce a more practical
task, i.e., Source-Free Occlusion-Aware Seamless Segmentation (SFOASS), and
propose its first solution, called UNconstrained Learning Omni-Context
Knowledge (UNLOCK). Specifically, UNLOCK includes two key modules: Omni
Pseudo-Labeling Learning and Amodal-Driven Context Learning. While adapting
without relying on source data or target labels, this framework enhances models
to achieve segmentation with 360{\deg} viewpoint coverage and occlusion-aware
reasoning. Furthermore, we benchmark the proposed SFOASS task through both
real-to-real and synthetic-to-real adaptation settings. Experimental results
show that our source-free method achieves performance comparable to
source-dependent methods, yielding state-of-the-art scores of 10.9 in mAAP and
11.6 in mAP, along with an absolute improvement of +4.3 in mAPQ over the
source-only method. All data and code will be made publicly available at
https://github.com/yihong-97/UNLOCK.
comment: Accepted to ICCV 2025. All data and code will be made publicly
available at https://github.com/yihong-97/UNLOCK
★ Out-of-Distribution Semantic Occupancy Prediction
Yuheng Zhang, Mengfei Duan, Kunyu Peng, Yuhang Wang, Ruiping Liu, Fei Teng, Kai Luo, Zhiyong Li, Kailun Yang
3D Semantic Occupancy Prediction is crucial for autonomous driving, providing
a dense, semantically rich environmental representation. However, existing
methods focus on in-distribution scenes, making them susceptible to
Out-of-Distribution (OoD) objects and long-tail distributions, which increases
the risk of undetected anomalies and misinterpretations, posing safety hazards.
To address these challenges, we introduce Out-of-Distribution Semantic
Occupancy Prediction, targeting OoD detection in 3D voxel space. To fill the
gaps in the dataset, we propose a Synthetic Anomaly Integration Pipeline that
injects synthetic anomalies while preserving realistic spatial and occlusion
patterns, enabling the creation of two datasets: VAA-KITTI and VAA-KITTI-360.
We introduce OccOoD, a novel framework integrating OoD detection into 3D
semantic occupancy prediction, with Voxel-BEV Progressive Fusion (VBPF)
leveraging an RWKV-based branch to enhance OoD detection via geometry-semantic
fusion. Experimental results demonstrate that OccOoD achieves state-of-the-art
OoD detection with an AuROC of 67.34% and an AuPRCr of 29.21% within a 1.2m
region, while maintaining competitive occupancy prediction performance. The
established datasets and source code will be made publicly available at
https://github.com/7uHeng/OccOoD.
comment: The established datasets and source code will be made publicly
available at https://github.com/7uHeng/OccOoD
★ UAIbot: Beginner-friendly web-based simulator for interactive robotics learning and research
This paper presents UAIbot, a free and open-source web-based robotics
simulator designed to address the educational and research challenges
conventional simulation platforms generally face. The Python and JavaScript
interfaces of UAIbot enable accessible hands-on learning experiences without
cumbersome installations. By allowing users to explore fundamental mathematical
and physical principles interactively, ranging from manipulator kinematics to
pedestrian flow dynamics, UAIbot provides an effective tool for deepening
student understanding, facilitating rapid experimentation, and enhancing
research dissemination.
comment: 12 pages, 8 figures, submitted to Springer proceedings
★ GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction ICML 2025
Trajectory prediction for surrounding agents is a challenging task in
autonomous driving due to its inherent uncertainty and underlying
multimodality. Unlike prevailing data-driven methods that primarily rely on
supervised learning, in this paper, we introduce a novel Graph-oriented Inverse
Reinforcement Learning (GoIRL) framework, which is an IRL-based predictor
equipped with vectorized context representations. We develop a feature adaptor
to effectively aggregate lane-graph features into grid space, enabling seamless
integration with the maximum entropy IRL paradigm to infer the reward
distribution and obtain the policy that can be sampled to induce multiple
plausible plans. Furthermore, conditioned on the sampled plans, we implement a
hierarchical parameterized trajectory generator with a refinement module to
enhance prediction accuracy and a probability fusion strategy to boost
prediction confidence. Extensive experimental results showcase our approach not
only achieves state-of-the-art performance on the large-scale Argoverse &
nuScenes motion forecasting benchmarks but also exhibits superior
generalization abilities compared to existing supervised models.
comment: Accepted by ICML 2025
★ CURL-SLAM: Continuous and Compact LiDAR Mapping
This paper studies 3D LiDAR mapping with a focus on developing an updatable
and localizable map representation that enables continuity, compactness and
consistency in 3D maps. Traditional LiDAR Simultaneous Localization and Mapping
(SLAM) systems often rely on 3D point cloud maps, which typically require
extensive storage to preserve structural details in large-scale environments.
In this paper, we propose a novel paradigm for LiDAR SLAM by leveraging the
Continuous and Ultra-compact Representation of LiDAR (CURL) introduced in [1].
Our proposed LiDAR mapping approach, CURL-SLAM, produces compact 3D maps
capable of continuous reconstruction at variable densities using CURL's
spherical harmonics implicit encoding, and achieves global map consistency
after loop closure. Unlike popular Iterative Closest Point (ICP)-based LiDAR
odometry techniques, CURL-SLAM formulates LiDAR pose estimation as a unique
optimization problem tailored for CURL and extends it to local Bundle
Adjustment (BA), enabling simultaneous pose refinement and map correction.
Experimental results demonstrate that CURL-SLAM achieves state-of-the-art 3D
mapping quality and competitive LiDAR trajectory accuracy, delivering
sensor-rate real-time performance (10 Hz) on a CPU. We will release the
CURL-SLAM implementation to the community.
★ Control of Marine Robots in the Era of Data-Driven Intelligence
The control of marine robots has long relied on model-based methods grounded
in classical and modern control theory. However, the nonlinearity and
uncertainties inherent in robot dynamics, coupled with the complexity of marine
environments, have revealed the limitations of conventional control methods.
The rapid evolution of machine learning has opened new avenues for
incorporating data-driven intelligence into control strategies, prompting a
paradigm shift in the control of marine robots. This paper provides a review of
recent progress in marine robot control through the lens of this emerging
paradigm. The review covers both individual and cooperative marine robotic
systems, highlighting notable achievements in data-driven control of marine
robots and summarizing open-source resources that support the development and
validation of advanced control methods. Finally, several future perspectives
are outlined to guide research toward achieving high-level autonomy for marine
robots in real-world applications. This paper aims to serve as a roadmap toward
the next-generation control framework of marine robots in the era of
data-driven intelligence.
★ Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions IROS 2025
Imitation learning has emerged as a powerful paradigm in robot manipulation,
yet its generalization capability remains constrained by object-specific
dependencies in limited expert demonstrations. To address this challenge, we
propose knowledge-driven imitation learning, a framework that leverages
external structural semantic knowledge to abstract object representations
within the same category. We introduce a novel semantic keypoint graph as a
knowledge template and develop a coarse-to-fine template-matching algorithm
that optimizes both structural consistency and semantic similarity. Evaluated
on three real-world robotic manipulation tasks, our method achieves superior
performance, surpassing image-based diffusion policies with only one-quarter of
the expert demonstrations. Extensive experiments further demonstrate its
robustness across novel objects, backgrounds, and lighting conditions. This
work pioneers a knowledge-driven approach to data-efficient robotic learning in
real-world settings. Code and more materials are available on
https://knowledge-driven.github.io/.
comment: IROS 2025
★ V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling
Ensuring robust planning and decision-making under rare, diverse, and
visually degraded long-tail scenarios remains a fundamental challenge for
autonomous driving in urban environments. This issue becomes more critical in
cooperative settings, where vehicles and infrastructure jointly perceive and
reason across complex environments. To address this challenge, we propose
V2X-REALM, a vision-language model (VLM)-based framework with adaptive
multimodal learning for robust cooperative autonomous driving under long-tail
scenarios. V2X-REALM introduces three core innovations: (i) a prompt-driven
long-tail scenario generation and evaluation pipeline that leverages foundation
models to synthesize realistic long-tail conditions such as snow and fog across
vehicle- and infrastructure-side views, enriching training diversity
efficiently; (ii) a gated multi-scenario adaptive attention module that
modulates the visual stream using scenario priors to recalibrate ambiguous or
corrupted features; and (iii) a multi-task scenario-aware contrastive learning
objective that improves multimodal alignment and promotes cross-scenario
feature separability. Extensive experiments demonstrate that V2X-REALM
significantly outperforms existing baselines in robustness, semantic reasoning,
safety, and planning accuracy under complex, challenging driving conditions,
advancing the scalability of end-to-end cooperative autonomous driving.
★ STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner
Zhou Tianxing, Wang Zhirui, Ao Haojia, Chen Guangyan, Xing Boyang, Cheng Jingwen, Yang Yi, Yue Yufeng
The ability to perform reliable long-horizon task planning is crucial for
deploying robots in real-world environments. However, directly employing Large
Language Models (LLMs) as action sequence generators often results in low
success rates due to their limited reasoning ability for long-horizon embodied
tasks. In the STEP framework, we construct a subgoal tree through a pair of
closed-loop models: a subgoal decomposition model and a leaf node termination
model. Within this framework, we develop a hierarchical tree structure that
spans from coarse to fine resolutions. The subgoal decomposition model
leverages a foundation LLM to break down complex goals into manageable
subgoals, thereby spanning the subgoal tree. The leaf node termination model
provides real-time feedback based on environmental states, determining when to
terminate the tree spanning and ensuring each leaf node can be directly
converted into a primitive action. Experiments conducted in both the
VirtualHome WAH-NL benchmark and on real robots demonstrate that STEP achieves
long-horizon embodied task completion with success rates up to 34% (WAH-NL) and
25% (real robot) outperforming SOTA methods.
★ Fault-Tolerant Spacecraft Attitude Determination using State Estimation Techniques
The extended and unscented Kalman filter, and the particle filter provide a
robust framework for fault-tolerant attitude estimation on spacecraft. This
paper explores how each filter performs for a large satellite in a low earth
orbit. Additionally, various techniques, built on these filters, for fault
detection, isolation and recovery from erroneous sensor measurements, are
analyzed. Key results from this analysis include filter performance for various
fault modes.
comment: 8 pages, 19 figures
★ Our Coding Adventure: Using LLMs to Personalise the Narrative of a Tangible Programming Robot for Preschoolers
Finding balanced ways to employ Large Language Models (LLMs) in education is
a challenge due to inherent risks of poor understanding of the technology and
of a susceptible audience. This is particularly so with younger children, who
are known to have difficulties with pervasive screen time. Working with a
tangible programming robot called Cubetto, we propose an approach to benefit
from the capabilities of LLMs by employing such models in the preparation of
personalised storytelling, necessary for preschool children to get accustomed
to the practice of commanding the robot. We engage in action research to
develop an early version of a formalised process to rapidly prototype game
stories for Cubetto. Our approach has both reproducible results, because it
employs open weight models, and is model-agnostic, because we test it with 5
different LLMs. We document on one hand the process, the used materials and
prompts, and on the other the learning experience and outcomes. We deem the
generation successful for the intended purposes of using the results as a
teacher aid. Testing the models on 4 different task scenarios, we encounter
issues of consistency and hallucinations and document the corresponding
evaluation process and attempts (some successful and some not) to overcome
these issues. Importantly, the process does not expose children to LLMs
directly. Rather, the technology is used to help teachers easily develop
personalised narratives on children's preferred topics. We believe our method
is adequate for preschool classes and we are planning to further experiment in
real-world educational settings.
comment: accepted at D-SAIL Workshop - Transformative Curriculum Design:
Digitalization, Sustainability, and AI Literacy for 21st Century Learning
★ ThermalDiffusion: Visual-to-Thermal Image-to-Image Translation for Autonomous Navigation ICRA 2025
Autonomous systems rely on sensors to estimate the environment around them.
However, cameras, LiDARs, and RADARs have their own limitations. In nighttime
or degraded environments such as fog, mist, or dust, thermal cameras can
provide valuable information regarding the presence of objects of interest due
to their heat signature. They make it easy to identify humans and vehicles that
are usually at higher temperatures compared to their surroundings. In this
paper, we focus on the adaptation of thermal cameras for robotics and
automation, where the biggest hurdle is the lack of data. Several multi-modal
datasets are available for driving robotics research in tasks such as scene
segmentation, object detection, and depth estimation, which are the cornerstone
of autonomous systems. However, they are found to be lacking in thermal
imagery. Our paper proposes a solution to augment these datasets with synthetic
thermal data to enable widespread and rapid adaptation of thermal cameras. We
explore the use of conditional diffusion models to convert existing RGB images
to thermal images using self-attention to learn the thermal properties of
real-world objects.
comment: Accepted at Thermal Infrared in Robotics (TIRO) Workshop, ICRA 2025
★ Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Tian-Yu Xiang, Ao-Qun Jin, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Sheng-Bin Duan, Fu-Chao Xie, Wen-Kai Wang, Si-Cheng Wang, Ling-Yun Li, Tian Tu, Zeng-Guang Hou
Vision-language-action (VLA) models extend vision-language models (VLM) by
integrating action generation modules for robotic manipulation. Leveraging
strengths of VLM in vision perception and instruction understanding, VLA models
exhibit promising generalization across diverse manipulation tasks. However,
applications demanding high precision and accuracy reveal performance gaps
without further adaptation. Evidence from multiple domains highlights the
critical role of post-training to align foundational models with downstream
applications, spurring extensive research on post-training VLA models. VLA
model post-training aims to address the challenge of improving an embodiment's
ability to interact with the environment for the given tasks, analogous to the
process of humans motor skills acquisition. Accordingly, this paper reviews
post-training strategies for VLA models through the lens of human motor
learning, focusing on three dimensions: environments, embodiments, and tasks. A
structured taxonomy is introduced aligned with human learning mechanisms: (1)
enhancing environmental perception, (2) improving embodiment awareness, (3)
deepening task comprehension, and (4) multi-component integration. Finally, key
challenges and trends in post-training VLA models are identified, establishing
a conceptual framework to guide future research. This work delivers both a
comprehensive overview of current VLA model post-training methods from a human
motor learning perspective and practical insights for VLA model development.
(Project website: https://github.com/AoqunJin/Awesome-VLA-Post-Training)
★ Cooperative Circumnavigation for Multi-Quadrotor Systems via Onboard Sensing
A cooperative circumnavigation framework is proposed for multi-quadrotor
systems to enclose and track a moving target without reliance on external
localization systems. The distinct relationships between quadrotor-quadrotor
and quadrotor-target interactions are evaluated using a heterogeneous
perception strategy and corresponding state estimation algorithms. A modified
Kalman filter is developed to fuse visual-inertial odometry with range
measurements to enhance the accuracy of inter-quadrotor relative localization.
An event-triggered distributed Kalman filter is designed to achieve robust
target state estimation under visual occlusion by incorporating neighbor
measurements and estimated inter-quadrotor relative positions. Using the
estimation results, a cooperative circumnavigation controller is constructed,
leveraging an oscillator-based autonomous formation flight strategy. We conduct
extensive indoor and outdoor experiments to validate the efficiency of the
proposed circumnavigation framework in occluded environments. Furthermore, a
quadrotor failure experiment highlights the inherent fault tolerance property
of the proposed framework, underscoring its potential for deployment in
search-and-rescue operations.
comment: 8 Pages, 7 figures. Accepted by RA-L
★ Effect of Haptic Feedback on Avoidance Behavior and Visual Exploration in Dynamic VR Pedestrian Environment
Kyosuke Ishibashi, Atsushi Saito, Zin Y. Tun, Lucas Ray, Megan C. Coram, Akihiro Sakurai, Allison M. Okamura, Ko Yamamoto
Human crowd simulation in virtual reality (VR) is a powerful tool with
potential applications including emergency evacuation training and assessment
of building layout. While haptic feedback in VR enhances immersive experience,
its effect on walking behavior in dense and dynamic pedestrian flows is
unknown. Through a user study, we investigated how haptic feedback changes user
walking motion in crowded pedestrian flows in VR. The results indicate that
haptic feedback changed users' collision avoidance movements, as measured by
increased walking trajectory length and change in pelvis angle. The
displacements of users' lateral position and pelvis angle were also increased
in the instantaneous response to a collision with a non-player character (NPC),
even when the NPC was inside the field of view. Haptic feedback also enhanced
users' awareness and visual exploration when an NPC approached from the side
and back. Furthermore, variation in walking speed was increased by the haptic
feedback. These results suggested that the haptic feedback enhanced users'
sensitivity to a collision in VR environment.
♻ ★ Finding the Easy Way Through -- the Probabilistic Gap Planner for Social Robot Navigation
Malte Probst, Raphael Wenzel, Tim Puphal, Monica Dasi, Nico A. Steinhardt, Sango Matsuzaki, Misa Komuro
In Social Robot Navigation, autonomous agents need to resolve many sequential
interactions with other agents. State-of-the art planners can efficiently
resolve the next, imminent interaction cooperatively and do not focus on longer
planning horizons. This makes it hard to maneuver scenarios where the agent
needs to select a good strategy to find gaps or channels in the crowd. We
propose to decompose trajectory planning into two separate steps: Conflict
avoidance for finding good, macroscopic trajectories, and cooperative collision
avoidance (CCA) for resolving the next interaction optimally. We propose the
Probabilistic Gap Planner (PGP) as a conflict avoidance planner. PGP modifies
an established probabilistic collision risk model to include a general
assumption of cooperativity. PGP biases the short-term CCA planner to head
towards gaps in the crowd. In extensive simulations with crowds of varying
density, we show that using PGP in addition to state-of-the-art CCA planners
improves the agents' performance: On average, agents keep more space to others,
create less tension, and cause fewer collisions. This typically comes at the
expense of slightly longer paths. PGP runs in real-time on WaPOCHI mobile robot
by Honda R&D.
♻ ★ ReactEMG: Zero-Shot, Low-Latency Intent Detection via sEMG
Runsheng Wang, Xinyue Zhu, Ava Chen, Jingxi Xu, Lauren Winterbottom, Dawn M. Nilsen, Joel Stein, Matei Ciocarlie
Surface electromyography (sEMG) signals show promise for effective
human-computer interfaces, particularly in rehabilitation and prosthetics.
However, challenges remain in developing systems that respond quickly and
reliably to user intent, across different subjects and without requiring
time-consuming calibration. In this work, we propose a framework for EMG-based
intent detection that addresses these challenges. Unlike traditional gesture
recognition models that wait until a gesture is completed before classifying
it, our approach uses a segmentation strategy to assign intent labels at every
timestep as the gesture unfolds. We introduce a novel masked modeling strategy
that aligns muscle activations with their corresponding user intents, enabling
rapid onset detection and stable tracking of ongoing gestures. In evaluations
against baseline methods, considering both accuracy and stability for device
control, our approach surpasses state-of-the-art performance in zero-shot
transfer conditions, demonstrating its potential for wearable robotics and
next-generation prosthetic systems. Our project page is available at:
https://reactemg.github.io
♻ ★ Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception IROS 2025
Deep object pose estimators are notoriously overconfident. A grasping agent
that both estimates the 6-DoF pose of a target object and predicts the
uncertainty of its own estimate could avoid task failure by choosing not to act
under high uncertainty. Even though object pose estimation improves and
uncertainty quantification research continues to make strides, few studies have
connected them to the downstream task of robotic grasping. We propose a method
for training lightweight, deep networks to predict whether a grasp guided by an
image-based pose estimate will succeed before that grasp is attempted. We
generate training data for our networks via object pose estimation on real
images and simulated grasping. We also find that, despite high object
variability in grasping trials, networks benefit from training on all objects
jointly, suggesting that a diverse variety of objects can nevertheless
contribute to the same goal.
comment: Accepted to IROS 2025
♻ ★ Learning Efficient and Robust Language-conditioned Manipulation using Textual-Visual Relevancy and Equivariant Language Mapping
Mingxi Jia, Haojie Huang, Zhewen Zhang, Chenghao Wang, Linfeng Zhao, Dian Wang, Jason Xinyu Liu, Robin Walters, Robert Platt, Stefanie Tellex
Controlling robots through natural language is pivotal for enhancing
human-robot collaboration and synthesizing complex robot behaviors. Recent
works that are trained on large robot datasets show impressive generalization
abilities. However, such pretrained methods are (1) often fragile to unseen
scenarios, and (2) expensive to adapt to new tasks. This paper introduces
Grounded Equivariant Manipulation (GEM), a robust yet efficient approach that
leverages pretrained vision-language models with equivariant language mapping
for language-conditioned manipulation tasks. Our experiments demonstrate GEM's
high sample efficiency and generalization ability across diverse tasks in both
simulation and the real world. GEM achieves similar or higher performance with
orders of magnitude fewer robot data compared with major data-efficient
baselines such as CLIPort and VIMA. Finally, our approach demonstrates greater
robustness compared to large VLA model, e.g, OpenVLA, at correctly interpreting
natural language commands on unseen objects and poses. Code, data, and training
details are available https://saulbatman.github.io/gem_page/
♻ ★ 3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different Sensors IROS 2025
Matteo Sodano, Federico Magistri, Elias Marks, Fares Hosn, Aibek Zurbayev, Rodrigo Marcuzzi, Meher V. R. Malladi, Jens Behley, Cyrill Stachniss
Crop yield estimation is a relevant problem in agriculture, because an
accurate yield estimate can support farmers' decisions on harvesting or
precision intervention. Robots can help to automate this process. To do so,
they need to be able to perceive the surrounding environment to identify target
objects such as trees and plants. In this paper, we introduce a novel approach
to address the problem of hierarchical panoptic segmentation of apple orchards
on 3D data from different sensors. Our approach is able to simultaneously
provide semantic segmentation, instance segmentation of trunks and fruits, and
instance segmentation of trees (a trunk with its fruits). This allows us to
identify relevant information such as individual plants, fruits, and trunks,
and capture the relationship among them, such as precisely estimate the number
of fruits associated to each tree in an orchard. To efficiently evaluate our
approach for hierarchical panoptic segmentation, we provide a dataset designed
specifically for this task. Our dataset is recorded in Bonn, Germany, in a real
apple orchard with a variety of sensors, spanning from a terrestrial laser
scanner to a RGB-D camera mounted on different robots platforms. The
experiments show that our approach surpasses state-of-the-art approaches in 3D
panoptic segmentation in the agricultural domain, while also providing full
hierarchical panoptic segmentation. Our dataset is publicly available at
https://www.ipb.uni-bonn.de/data/hops/. The open-source implementation of our
approach is available at https://github.com/PRBonn/hapt3D.
comment: Accepted to IROS 2025
♻ ★ Rapid Gyroscope Calibration: A Deep Learning Approach
Low-cost gyroscope calibration is essential for ensuring the accuracy and
reliability of gyroscope measurements. Stationary calibration estimates the
deterministic parts of measurement errors. To this end, a common practice is to
average the gyroscope readings during a predefined period and estimate the
gyroscope bias. Calibration duration plays a crucial role in performance,
therefore, longer periods are preferred. However, some applications require
quick startup times and calibration is therefore allowed only for a short time.
In this work, we focus on reducing low-cost gyroscope calibration time using
deep learning methods. We propose an end-to-end convolutional neural network
for the application of gyroscope calibration. We explore the possibilities of
using multiple real and virtual gyroscopes to improve the calibration
performance of single gyroscopes. To train and validate our approach, we
recorded a dataset consisting of 186.6 hours of gyroscope readings, using 36
gyroscopes of four different brands. We also created a virtual dataset
consisting of simulated gyroscope readings. The six datasets were used to
evaluate our proposed approach. One of our key achievements in this work is
reducing gyroscope calibration time by up to 89% using three low-cost
gyroscopes. Our dataset is publicly available to allow reproducibility of our
work and to increase research in the field.
comment: 10 Pages, 14 Figures
♻ ★ PCF-Grasp: Converting Point Completion to Geometry Feature to Enhance 6-DoF Grasp
The 6-Degree of Freedom (DoF) grasp method based on point clouds has shown
significant potential in enabling robots to grasp target objects. However, most
existing methods are based on the point clouds (2.5D points) generated from
single-view depth images. These point clouds only have one surface side of the
object providing incomplete geometry information, which mislead the grasping
algorithm to judge the shape of the target object, resulting in low grasping
accuracy. Humans can accurately grasp objects from a single view by leveraging
their geometry experience to estimate object shapes. Inspired by humans, we
propose a novel 6-DoF grasping framework that converts the point completion
results as object shape features to train the 6-DoF grasp network. Here, point
completion can generate approximate complete points from the 2.5D points
similar to the human geometry experience, and converting it as shape features
is the way to utilize it to improve grasp efficiency. Furthermore, due to the
gap between the network generation and actual execution, we integrate a score
filter into our framework to select more executable grasp proposals for the
real robot. This enables our method to maintain a high grasp quality in any
camera viewpoint. Extensive experiments demonstrate that utilizing complete
point features enables the generation of significantly more accurate grasp
proposals and the inclusion of a score filter greatly enhances the credibility
of real-world robot grasping. Our method achieves a 17.8\% success rate higher
than the state-of-the-art method in real-world experiments.
♻ ★ RAMBO: RL-augmented Model-based Whole-body Control for Loco-manipulation
Loco-manipulation, physical interaction of various objects that is
concurrently coordinated with locomotion, remains a major challenge for legged
robots due to the need for both precise end-effector control and robustness to
unmodeled dynamics. While model-based controllers provide precise planning via
online optimization, they are limited by model inaccuracies. In contrast,
learning-based methods offer robustness, but they struggle with precise
modulation of interaction forces. We introduce RAMBO, a hybrid framework that
integrates model-based whole-body control within a feedback policy trained with
reinforcement learning. The model-based module generates feedforward torques by
solving a quadratic program, while the policy provides feedback corrective
terms to enhance robustness. We validate our framework on a quadruped robot
across a diverse set of real-world loco-manipulation tasks, such as pushing a
shopping cart, balancing a plate, and holding soft objects, in both quadrupedal
and bipedal walking. Our experiments demonstrate that RAMBO enables precise
manipulation capabilities while achieving robust and dynamic locomotion.
♻ ★ The Starlink Robot: A Platform and Dataset for Mobile Satellite Communication
The integration of satellite communication into mobile devices represents a
paradigm shift in connectivity, yet the performance characteristics under
motion and environmental occlusion remain poorly understood. We present the
Starlink Robot, the first mobile robotic platform equipped with Starlink
satellite internet, comprehensive sensor suite including upward-facing camera,
LiDAR, and IMU, designed to systematically study satellite communication
performance during movement. Our multi-modal dataset captures synchronized
communication metrics, motion dynamics, sky visibility, and 3D environmental
context across diverse scenarios including steady-state motion, variable
speeds, and different occlusion conditions. This platform and dataset enable
researchers to develop motion-aware communication protocols, predict
connectivity disruptions, and optimize satellite communication for emerging
mobile applications from smartphones to autonomous vehicles. The project is
available at https://github.com/StarlinkRobot.
♻ ★ CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance
We introduce CREStE, a scalable learning-based mapless navigation framework
to address the open-world generalization and robustness challenges of outdoor
urban navigation. Key to achieving this is learning perceptual representations
that generalize to open-set factors (e.g. novel semantic classes, terrains,
dynamic entities) and inferring expert-aligned navigation costs from limited
demonstrations. CREStE addresses both these issues, introducing 1) a visual
foundation model (VFM) distillation objective for learning open-set structured
bird's-eye-view perceptual representations, and 2) counterfactual inverse
reinforcement learning (IRL), a novel active learning formulation that uses
counterfactual trajectory demonstrations to reason about the most important
cues when inferring navigation costs. We evaluate CREStE on the task of
kilometer-scale mapless navigation in a variety of city, offroad, and
residential environments and find that it outperforms all state-of-the-art
approaches with 70% fewer human interventions, including a 2-kilometer mission
in an unseen environment with just 1 intervention; showcasing its robustness
and effectiveness for long-horizon mapless navigation. Videos and additional
materials can be found on the project page: https://amrl.cs.utexas.edu/creste
comment: 18 pages, 10 figures, 5 tables
♻ ★ IMPACT: Behavioral Intention-aware Multimodal Trajectory Prediction with Adaptive Context Trimming
Jiawei Sun, Xibin Yue, Jiahui Li, Tianle Shen, Chengran Yuan, Shuo Sun, Sheng Guo, Quanyun Zhou, Marcelo H Ang Jr
While most prior research has focused on improving the precision of
multimodal trajectory predictions, the explicit modeling of multimodal
behavioral intentions (e.g., yielding, overtaking) remains relatively
underexplored. This paper proposes a unified framework that jointly predicts
both behavioral intentions and trajectories to enhance prediction accuracy,
interpretability, and efficiency. Specifically, we employ a shared context
encoder for both intention and trajectory predictions, thereby reducing
structural redundancy and information loss. Moreover, we address the lack of
ground-truth behavioral intention labels in mainstream datasets (Waymo,
Argoverse) by auto-labeling these datasets, thus advancing the community's
efforts in this direction. We further introduce a vectorized occupancy
prediction module that infers the probability of each map polyline being
occupied by the target vehicle's future trajectory. By leveraging these
intention and occupancy prediction priors, our method conducts dynamic,
modality-dependent pruning of irrelevant agents and map polylines in the
decoding stage, effectively reducing computational overhead and mitigating
noise from non-critical elements. Our approach ranks first among LiDAR-free
methods on the Waymo Motion Dataset and achieves first place on the Waymo
Interactive Prediction Dataset. Remarkably, even without model ensembling, our
single-model framework improves the soft mean average precision (softmAP) by 10
percent compared to the second-best method in the Waymo Interactive Prediction
Leaderboard. Furthermore, the proposed framework has been successfully deployed
on real vehicles, demonstrating its practical effectiveness in real-world
applications.
comment: under review
♻ ★ EFEAR-4D: Ego-Velocity Filtering for Efficient and Accurate 4D radar Odometry
Odometry is a crucial component for successfully implementing autonomous
navigation, relying on sensors such as cameras, LiDARs and IMUs. However, these
sensors may encounter challenges in extreme weather conditions, such as
snowfall and fog. The emergence of FMCW radar technology offers the potential
for robust perception in adverse conditions. As the latest generation of FWCW
radars, the 4D mmWave radar provides point cloud with range, azimuth,
elevation, and Doppler velocity information, despite inherent sparsity and
noises in the point cloud. In this paper, we propose EFEAR-4D, an accurate,
highly efficient, and learning-free method for large-scale 4D radar odometry
estimation. EFEAR-4D exploits Doppler velocity information delicately for
robust ego-velocity estimation, resulting in a highly accurate prior guess.
EFEAR-4D maintains robustness against point-cloud sparsity and noises across
diverse environments through dynamic object removal and effective region-wise
feature extraction. Extensive experiments on two publicly available 4D radar
datasets demonstrate state-of-the-art reliability and localization accuracy of
EFEAR-4D under various conditions. Furthermore, we have collected a dataset
following the same route but varying installation heights of the 4D radar,
emphasizing the significant impact of radar height on point cloud quality - a
crucial consideration for real-world deployments. Our algorithm and dataset
will be available soon at https://github.com/CLASS-Lab/EFEAR-4D.
♻ ★ What Foundation Models can Bring for Robot Learning in Manipulation : A Survey
Dingzhe Li, Yixiang Jin, Yuhao Sun, Yong A, Hongze Yu, Jun Shi, Xiaoshuai Hao, Peng Hao, Huaping Liu, Fuchun Sun, Jianwei Zhang, Bin Fang
The realization of universal robots is an ultimate goal of researchers.
However, a key hurdle in achieving this goal lies in the robots' ability to
manipulate objects in their unstructured surrounding environments according to
different tasks. The learning-based approach is considered an effective way to
address generalization. The impressive performance of foundation models in the
fields of computer vision and natural language suggests the potential of
embedding foundation models into manipulation tasks as a viable path toward
achieving general manipulation capability. However, we believe achieving
general manipulation capability requires an overarching framework akin to auto
driving. This framework should encompass multiple functional modules, with
different foundation models assuming distinct roles in facilitating general
manipulation capability. This survey focuses on the contributions of foundation
models to robot learning for manipulation. We propose a comprehensive framework
and detail how foundation models can address challenges in each module of the
framework. What's more, we examine current approaches, outline challenges,
suggest future research directions, and identify potential risks associated
with integrating foundation models into this domain.