arXiv:2508. 02425v2 Announce Type: replace-cross Abstract: In physical human-robot collaboration (pHRC) settings, humans and robots collaborate directly in shared environments.
arXiv:2508. 12435v2 Announce Type: replace-cross Abstract: While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors.
arXiv:2606. 24450v1 Announce Type: cross Abstract: Perceiving physical contact is fundamental to dexterous manipulation.
arXiv:2510. 25960v2 Announce Type: replace-cross Abstract: In this paper, we present a framework that uses acoustic side-channel analysis (ASCA) to monitor and verify whether a robot correctly executes its intended commands.
arXiv:2510. 01711v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained Vision-Language Models (VLMs).
arXiv:2606. 01950v1 Announce Type: cross Abstract: World models enable intelligent agents to predict the consequences of their actions on the environment.
arXiv:2607. 00530v1 Announce Type: cross Abstract: Improvements in the technical performance of human--robot interaction (HRI) systems do not automatically translate into differences that human users can detect during live interaction.
Predicting object dynamics (i. e.
arXiv:2605. 31286v2 Announce Type: replace-cross Abstract: Real-world household robots require Vision-Language-Action (VLA) foundation models that can acquire reusable manipulation skills across diverse objects, task conditions, and household environments.
arXiv:2607. 14675v1 Announce Type: cross Abstract: Robust human-robot interaction in complex environments requires accurate gesture perception, semantic scene understanding, and reliable task planning under limited onboard computing resources.
arXiv:2606. 31694v1 Announce Type: cross Abstract: For robots manipulating open-world objects, tactile representations must generalize to unseen materials.
arXiv:2606. 17054v1 Announce Type: cross Abstract: Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality.
arXiv:2602. 07341v2 Announce Type: replace Abstract: This paper focuses on the scalable robot learning for manipulation in the dexterous robot arm-hand systems, where the remote human-robot interactions via augmented reality (AR) are established to collect the expert demonstration data for improving efficiency.