A Novel Robust Imitation Learning Framework for Dual-Arm Object-Moving Tasks

Drawing inspiration from the mechanism of human skill acquisition, imitation learning has demonstrated remarkable performance. Over recent years, modelbased imitation learning combined with machine learning and control theory has been continuously developed and adapted to unstructured environments. However, most results for dual-arm tasks focus on relatively safe and stable environments, which still lack robustness to generalize skills. In this article, we propose a novel robust imitation learning framework for dual-arm object-moving tasks. During demonstration, we present a shared teleoperation strategy that actively assists the operator in remotely executing dual-arm tasks, aiming to reduce the operational difficulty and stress. During modeling and generalization, we propose a coupled linear parameter-varying dynamical system (CLPV-DS), which possesses the ability to protect and restore states against possible disturbances in the environment while maintaining good tracking accuracy and stability. To address the risk of box slipping caused by disturbances, we further introduce amutual following strategy, enabling the arms to compliantly follow each other while maintaining appropriate contact force. Considering potential obstacles in a complex generalization environment, we introduce a reactive obstacle avoidance strategy in real time that ensures global asymptotic stability. Finally, we verified the effectiveness of the proposed framework through comprehensive testing in both 2-D simulations and real-robot experiments.