Physical Human-Robot Interaction for Grasping in Augmented Reality via Rigid-Soft Robot Synergy
Huishi Huang, Jack Klusmann, Haozhe Wang, Shuchen Ji, Fengkang Ying, Yiyuan Zhang, John Nassour, Gordon Cheng, Daniela Rus, Jun Liu, Marcelo H Ang, Cecilia Laschi
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
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.
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