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arXiv Machine Learning

General Performance Guarantee for Human Torque Estimation-Based Task-Agnostic Assistive Exoskeleton Control

The paper presents a theoretical framework for human torque estimation-based task‑agnostic control of robotic exoskeletons, defining matched assistance as scenarios where the robot positively contributes to human movement. It designs the robot’s desired interaction torque to guarantee a lower bound on matched assistance probability across the entire torque distribution, including unseen data. Experimental validation on the ABLE upper‑limb exoskeleton shows that the strategy achieves smooth movement and reduced human effort across multiple tasks.

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arXiv Machine Learning
Aug 27

Generative Action-Chunk Sampling for Adaptive Stiffness Control in Physical Human-Robot Collaboration

The paper introduces an adaptive stiffness control framework for physical human‑robot collaboration that uses generative action‑chunk sampling conditioned on RGB images and joint‑torque estimates. By sampling multiple future action chunks, the system measures variation among them to adjust joint stiffness and damping in real time—higher variation leads to more compliance, while lower variation yields firmer assistance. In a collaborative transport experiment with four possible directions, the method achieved a 0.95 success rate, outperforming a fixed‑stiffness baseline (0.83) and a deterministic baseline (0.69).

By Aoi Otake, Ferdinand Hartmann, Ko Igari, Shingo Murata
roboticsbenchmarks
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arXiv Machine Learning
Aug 18

Learning Varying Physical Therapist-Patient Interactions for Robot-mediated Upper Limb Task-Specific Training

arXiv:2608. 15995v1 Announce Type: cross Abstract: Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage.

By Jia Quan Loh (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Vincent Crocher (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Marlena Klaic (Melbourne School of Health Sciences, The University of Melbourne), Denny Oetomo (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne), Ying Tan (Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne)
roboticsbenchmarks
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arXiv AI
Sep 4

FWBC-VLA: Force-Aware Whole-Body Compensation for Contact-Rich Loco-Manipulation

FWBC‑VLA is a force‑aware framework that links vision‑language‑action (VLA) models with whole‑body compensation control for wheeled‑legged robots. It introduces HSR‑Force, a sensorless residual‑torque estimator that infers contact strength and encodes this information as tokens for the VLA action decoder, allowing the policy to detect contact onset, loading, and release. The system fine‑tunes a pretrained VLA backbone on a large WL&Arm dataset, combines proprioceptive, Jacobian‑derived force, and contact estimates to generate corrective actions, and demonstrates effectiveness in real‑world tasks such as whiteboard wiping and door opening.

By Yutian Zhang, Siyuan Ma, Liwen Yang, Yang Li, Ce Hao, Haozhen Chi, Dong We, Qiaojun Yu, Dibo Hou
roboticsfine-tuningmultimodal
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arXiv AI
Jun 3

Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics

arXiv:2507. 21638v2 Announce Type: replace Abstract: The development of reinforcement learning (RL) algorithms has been largely driven by ambitious challenge tasks and benchmarks.

By Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius, Elle Miller, Trevor McInroe, Fan Zhang, Patricia Wollstadt, Stefano V. Albrecht, Subramanian Ramamoorthy
agentsreinforcement-learningroboticsbenchmarks
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arXiv Machine Learning
Jul 14

NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception

arXiv:2607. 11734v1 Announce Type: cross Abstract: Differentiable simulators have advanced policy learning and model-based control, yet actuator dynamics remain an important source of sim-to-real error.

By Zhiyang Dou, John U. Onyemelukwe, Hangxing Zhang, Heng Zhang, Minghao Guo, Yunsheng Tian, Michal Piotr Lipiec, Joshua Jacob, Chao Liu, Peter Yichen Chen, Yuri Ivanov, Wojciech Matusik
llmsroboticsbenchmarks
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arXiv AI
4d ago

Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordination

The paper introduces Watch, Infer, Coordinate, a benchmark and inference method for robots to deduce a partner’s physical constraints by observing joint behavior in physically coupled manipulation tasks. It demonstrates that understanding these constraints enables zero‑shot coordination on new tasks, achieving performance close to an oracle with true constraint knowledge. The study focuses on scenarios where a robot’s limitations may be unknown to its partner, such as hardware degradation or actuator faults.

By Suyu Ye, Zheyuan Zhang, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Tianmin Shu, Homanga Bharadhwaj, Nakul Agarwal
roboticsbenchmarks
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