異なる人型ロボットアーム間の距離指標に基づく模倣学習
Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms
形態の異なる2台の人型ロボットアーム間で、身体対応問題を距離指標で解決し、その指標を損失関数や強化学習のフィードバックとして用いて静的・動的な模倣を実現した。
著者: Marcus Ebner von Eschenbach, Binyamin Manela, Jan Peters, Armin Biess
分類: cs.RO, cs.LG, stat.ML
原文アブストラクト
The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential. One major challenge in imitation learning is the correspondence problem: how to establish corresponding states and actions between expert and learner, when the embodiments of the agents are different (morphology, dynamics, degrees of freedom, etc.). Many existing approaches in imitation learning circumvent the correspondence problem, for example, kinesthetic teaching or teleoperation, which are performed on the robot. In this work we explicitly address the correspondence problem by introducing a distance measure between dissimilar embodiments. This measure is then used as a loss function for static pose imitation and as a feedback signal within a model-free deep reinforcement learning framework for dynamic movement imitation between two anthropomorphic robotic arms in simulation. We find that the measure is well suited for describing the similarity between embodiments and for learning imitation policies by distance minimization.
