軌道最適化のデモンストレーションを用いたロバスト歩行のためのモデルフリー強化学習
Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization
軌道最適化で生成した単一のデモを初期探索に活用し、その後タスク報酬を直接最適化する2段階の強化学習で、実機に追加学習なしで展開可能なロバスト方策を四足ロボットの跳躍・走行タスクで実現した。
著者: Miroslav Bogdanovic, Majid Khadiv, Ludovic Righetti
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
We present a general, two-stage reinforcement learning approach to create robust policies that can be deployed on real robots without any additional training using a single demonstration generated by trajectory optimization. The demonstration is used in the first stage as a starting point to facilitate initial exploration. In the second stage, the relevant task reward is optimized directly and a policy robust to environment uncertainties is computed. We demonstrate and examine in detail the performance and robustness of our approach on highly dynamic hopping and bounding tasks on a quadruped robot.
