Yang, Xiao (Zhejiang University), Hou, Che (Midea Group Co., Ltd.), He, Yanhao (Midea Group Co., Ltd.), Zhang, Xiaoyu (Midea Group Co., Ltd.), Chen, Wenjie (University of California at Berkeley), Chen, Zheng (Zhejiang University)

Learning Variable Impedance Control for Contact-Rich Robotics Manipulation with Guaranteed Passivity

Scheduled for presentation during the Regular Session "AI-based Robot Control I" (WeAT2), Wednesday, July 16, 2025, 11:20−11:40, Room 106

Joint 10th IFAC Symposium on Mechatronic Systems and 14th Symposium on Robotics, July 15-18, 2025, Paris, France

This information is tentative and subject to change. Compiled on July 16, 2025

Abstract

In this paper, a learning variable impedance control is designed for contact-rich robotics manipulation with guaranteed passivity. Firstly, a reinforcement-learning-based variable impedance policy is proposed, where the impedance parameters are selected as action space to achieve both the position precision and contact compliance. Since the RL-output impedance parameters may violate the passivity condition, a passivity-guaranteed impedance parameter generator is then designed to ensure the passivity of the robotics system. Additionally, an adaptive robust variable impedance control (ARVIC) framework is developed with the adaptive robust technique to cope with nonlinearity and uncertainties, which also ensures transiently convergence to the impedance contact model. Finally, a connector insertion task is designed to evaluate the effectiveness of the proposed system.