深層強化学習による四足歩行ロボットの人間動作制御
Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning
人間の動作を四足ロボットの動作に変換し、強化学習でその動作を追従する制御方針を学習させることで、ユーザーが直感的に四足ロボットを操作できるシステムを提案した。
著者: Sunwoo Kim, Maks Sorokin, Jehee Lee, Sehoon Ha
分類: cs.RO
原文アブストラクト
A motion-based control interface promises flexible robot operations in dangerous environments by combining user intuitions with the robot's motor capabilities. However, designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is not straightforward because different dynamics and control strategies govern their movements. We propose a novel motion control system that allows a human user to operate various motor tasks seamlessly on a quadrupedal robot. We first retarget the captured human motion into the corresponding robot motion with proper semantics using supervised learning and post-processing techniques. Then we apply the motion imitation learning with curriculum learning to develop a control policy that can track the given retargeted reference. We further improve the performance of both motion retargeting and motion imitation by training a set of experts. As we demonstrate, a user can execute various motor tasks using our system, including standing, sitting, tilting, manipulating, walking, and turning, on simulated and real quadrupeds. We also conduct a set of studies to analyze the performance gain induced by each component.
