HERD：人間からロボットへの連続進化による模倣学習
HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration
人間の五指ロボットを徐々に商用ロボットへと進化させながら強化学習を行うことで、人間の実演から操作スキルを転移させる手法を提案した。
著者: Xingyu Liu, Deepak Pathak, Kris M. Kitani
分類: cs.RO, cs.AI, cs.LG, cs.NE
原文アブストラクト
The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the structure of the human hand can be very different from the desired robot gripper. In this work, we show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning, where a five-finger human dexterous hand robot gradually evolves into a commercial robot, while repeated interacting in a physics simulator to continuously update the policy that is first learned from human demonstration. To deal with the high dimensions of robot parameters, we propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy. Through experiments on human object manipulation datasets, we show that our framework can efficiently transfer the expert human agent policy trained from human demonstrations in diverse modalities to target commercial robots.
