誤差認識型ポリシー学習：部分観測可能な動的環境におけるゼロショット汎化
Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments
観測できない要因の影響を予測する誤差予測関数と同時学習する誤差認識型ポリシーを提案し、シミュレーションで訓練した股関節トルク支援装置のポリシーを未知の身体特性を持つ人間にゼロショットで転移できることを示した。
著者: Visak Kumar, Sehoon Ha, C. Karen Liu
分類: cs.RO, cs.LG
原文アブストラクト
Simulation provides a safe and efficient way to generate useful data for learning complex robotic tasks. However, matching simulation and real-world dynamics can be quite challenging, especially for systems that have a large number of unobserved or unmeasurable parameters, which may lie in the robot dynamics itself or in the environment with which the robot interacts. We introduce a novel approach to tackle such a sim-to-real problem by developing policies capable of adapting to new environments, in a zero-shot manner. Key to our approach is an error-aware policy (EAP) that is explicitly made aware of the effect of unobservable factors during training. An EAP takes as input the predicted future state error in the target environment, which is provided by an error-prediction function, simultaneously trained with the EAP. We validate our approach on an assistive walking device trained to help the human user recover from external pushes. We show that a trained EAP for a hip-torque assistive device can be transferred to different human agents with unseen biomechanical characteristics. In addition, we show that our method can be applied to other standard RL control tasks.
