有界極値探索を用いた分布シフト下でのロボット操作のための深層強化学習
Deep Reinforcement Learning for Robotic Manipulation under Distribution Shift with Bounded Extremum Seeking
強化学習ポリシーを有界極値探索と組み合わせ、訓練時と異なる環境条件（分布シフト）下でのロボット操作の堅牢性を向上させるハイブリッド制御手法を提案した。
詳しい要約
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著者: Shaifalee Saxena, Rafael Fierro, Alexander Scheinker
分類: cs.RO, cs.LG
原文アブストラクト
Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test conditions differ from the training distribution. This limitation is especially important in contact-rich tasks such as pushing and pick-and-place, where changes in goals, contact conditions, or robot dynamics can drive the system out-of-distribution at inference time. In this paper, we investigate a hybrid controller that combines reinforcement learning with bounded extremum seeking to improve robustness under such conditions. In the proposed approach, deep deterministic policy gradient (DDPG) policies are trained under standard conditions on the robotic pushing and pick-and-place tasks, and are then combined with bounded ES during deployment. The RL policy provides fast manipulation behavior, while bounded ES ensures robustness of the overall controller to time variations when operating conditions depart from those seen during training. The resulting controller is evaluated under several out-of-distribution settings, including time-varying goals and spatially varying friction patches.
