Physical Human-Robot Interaction for Grasping in Augmented Reality via Rigid-Soft Robot Synergy
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866911456938688512 |
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| author | Huang, Huishi Klusmann, Jack Wang, Haozhe Ji, Shuchen Ying, Fengkang Zhang, Yiyuan Nassour, John Cheng, Gordon Rus, Daniela Liu, Jun Ang Jr, Marcelo H Laschi, Cecilia |
| author_facet | Huang, Huishi Klusmann, Jack Wang, Haozhe Ji, Shuchen Ying, Fengkang Zhang, Yiyuan Nassour, John Cheng, Gordon Rus, Daniela Liu, Jun Ang Jr, Marcelo H Laschi, Cecilia |
| contents | Hybrid rigid-soft robots combine the precision of rigid manipulators with the compliance and adaptability of soft arms, offering a promising approach for versatile grasping in unstructured environments. However, coordinating hybrid robots remains challenging, due to difficulties in modeling, perception, and cross-domain kinematics. In this work, we present a novel augmented reality (AR)-based physical human-robot interaction framework that enables direct teleoperation of a hybrid rigid-soft robot for simple reaching and grasping tasks. Using an AR headset, users can interact with a simulated model of the robotic system integrated into a general-purpose physics engine, which is superimposed on the real system, allowing simulated execution prior to real-world deployment. To ensure consistent behavior between the virtual and physical robots, we introduce a real-to-simulation parameter identification pipeline that leverages the inherent geometric properties of the soft robot, enabling accurate modeling of its static and dynamic behavior as well as the control system's response. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17128 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physical Human-Robot Interaction for Grasping in Augmented Reality via Rigid-Soft Robot Synergy Huang, Huishi Klusmann, Jack Wang, Haozhe Ji, Shuchen Ying, Fengkang Zhang, Yiyuan Nassour, John Cheng, Gordon Rus, Daniela Liu, Jun Ang Jr, Marcelo H Laschi, Cecilia Robotics Hybrid rigid-soft robots combine the precision of rigid manipulators with the compliance and adaptability of soft arms, offering a promising approach for versatile grasping in unstructured environments. However, coordinating hybrid robots remains challenging, due to difficulties in modeling, perception, and cross-domain kinematics. In this work, we present a novel augmented reality (AR)-based physical human-robot interaction framework that enables direct teleoperation of a hybrid rigid-soft robot for simple reaching and grasping tasks. Using an AR headset, users can interact with a simulated model of the robotic system integrated into a general-purpose physics engine, which is superimposed on the real system, allowing simulated execution prior to real-world deployment. To ensure consistent behavior between the virtual and physical robots, we introduce a real-to-simulation parameter identification pipeline that leverages the inherent geometric properties of the soft robot, enabling accurate modeling of its static and dynamic behavior as well as the control system's response. |
| title | Physical Human-Robot Interaction for Grasping in Augmented Reality via Rigid-Soft Robot Synergy |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.17128 |