メタ強化学習によるシミュレーションから実機へのドメイン適応
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
メタ学習で多様な動的環境に適応できる方策を訓練し、タスク特化の軌道生成モデルで探索を効率化することで、シミュレーションから実機への転移を実現した。KUKAロボットでホッケーパックを目標に打つタスクに適用し、安定した適応性能を示した。
著者: Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, Ville Kyrki
分類: cs.CV, cs.RO
原文アブストラクト
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware. In this work, we propose to address the problem of sim-to-real domain transfer by using meta learning to train a policy that can adapt to a variety of dynamic conditions, and using a task-specific trajectory generation model to provide an action space that facilitates quick exploration. We evaluate the method by performing domain adaptation in simulation and analyzing the structure of the latent space during adaptation. We then deploy this policy on a KUKA LBR 4+ robot and evaluate its performance on a task of hitting a hockey puck to a target. Our method shows more consistent and stable domain adaptation than the baseline, resulting in better overall performance.
