微分可能シミュレーションを用いた知覚的四足歩行制御のための残差方策学習
Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation
微分可能シミュレーションによる一次方策勾配法に残差方策学習を組み合わせ、四足歩行の移動と知覚ナビゲーションを数分でエンドツーエンド学習できることを示した。
著者: Jing Yuan Luo, Yunlong Song, Victor Klemm, Fan Shi, Davide Scaramuzza, Marco Hutter
分類: cs.RO
原文アブストラクト
First-order Policy Gradient (FoPG) algorithms such as Backpropagation through Time and Analytical Policy Gradients leverage local simulation physics to accelerate policy search, significantly improving sample efficiency in robot control compared to standard model-free reinforcement learning. However, FoPG algorithms can exhibit poor learning dynamics in contact-rich tasks like locomotion. Previous approaches address this issue by alleviating contact dynamics via algorithmic or simulation innovations. In contrast, we propose guiding the policy search by learning a residual over a simple baseline policy. For quadruped locomotion, we find that the role of residual policy learning in FoPG-based training (FoPG RPL) is primarily to improve asymptotic rewards, compared to improving sample efficiency for model-free RL. Additionally, we provide insights on applying FoPG's to pixel-based local navigation, training a point-mass robot to convergence within seconds. Finally, we showcase the versatility of FoPG RPL by using it to train locomotion and perceptive navigation end-to-end on a quadruped in minutes.
