Contact-Rich SE(3)-Equivariant Robot Manipulation Task Learning via Geometric Impedance Control

作     者：Seo, Joohwan Prakash, Nikhil P. S. Zhang, Xiang Wang, Changhao Choi, Jongeun Tomizuka, Masayoshi Horowitz, Roberto 

作者机构：Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Yonsei Univ Sch Mech Engn Seoul 03722 South Korea 

出 版 物：《IEEE ROBOTICS AND AUTOMATION LETTERS》 (IEEE Robot. Autom.)

年 卷 期：2024年第9卷第2期

页      面：1508-1515页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：National Research Foundation of Korea 

主　　题：Machine learning for robot control compliance and impedance control learning from demonstration 

摘      要：This letter presents a differential geometric control approach that leverages SE(3) group invariance and equivariance to increase transferability in learning robot manipulation tasks that involve interaction with the environment. Specifically, we employ a control law and a learning representation framework that remain invariant under arbitrary SE(3) transformations of the manipulation task definition. Furthermore, the control law and learning representation framework are shown to be SE(3) equivariant when represented relative to the spatial frame. The proposed approach is based on utilizing a recently presented geometric impedance control (GIC) combined with a learning variable impedance control framework, where the gain scheduling policy is trained in a supervised learning fashion from expert demonstrations. A geometrically consistent error vector (GCEV) is fed to a neural network to achieve a gain scheduling policy that remains invariant to arbitrary translation and rotations. A comparison of our proposed control and learning framework with a well-known Cartesian space learning impedance control, equipped with a Cartesian error vector-based gain scheduling policy, confirms the significantly superior learning transferability of our proposed approach. A hardware implementation on a peg-in-hole task is conducted to validate the learning transferability and feasibility of the proposed approach.