人間同士のデモンストレーションから学ぶ人間らしいロボットインピーダンス調整スキル
Human-Like Robot Impedance Regulation Skill Learning from Human-Human Demonstrations
人間同士の協調デモからEMGと動作データを用いてインピーダンスプロファイルと参照軌道を抽出し、LSTMでタスク指向のインピーダンス調整方策を学習、全身インピーダンス制御で実装する枠組みを提案。
著者: Chenzui Li, Xi Wu, Yiming Chen, Tao Teng, Xuefeng Zhang, Sylvain Calinon, Darwin Caldwell, Fei Chen
分類: cs.RO
原文アブストラクト
Humans are experts in physical collaboration by leveraging cognitive abilities such as perception, reasoning, and decision-making to regulate compliance behaviors based on their partners' states and task requirements. Equipping robots with similar cognitive-inspired collaboration skills can significantly enhance the efficiency and adaptability of human-robot collaboration (HRC). This paper introduces an innovative HumanInspired Impedance Regulation Skill Learning framework (HIImpRSL) for robotic systems to achieve leader-follower and mutual adaptation in multiple physical collaborative tasks. The proposed framework enables the robot to adapt its compliance based on human states and reference trajectories derived from human-human demonstrations. By integrating electromyography (EMG) signals and motion data, we extract endpoint impedance profiles and reference trajectories to construct a joint representation via imitation learning. An LSTM-based module then learns task-oriented impedance regulation policies, which are implemented through a whole-body impedance controller for online impedance adaptation. Experimental validation was conducted through collaborative transportation, two interactive Tai Chi pushing hands, and collaborative sawing tasks with multiple human subjects, demonstrating the ability of our framework to achieve human-like collaboration skills and the superior performance from the perspective of interactive forces compared to four other related methods.
