最小限の人的労力で模倣学習を行うフレームワーク
A Framework for Learning from Demonstration with Minimal Human Effort
共有自律において、各エピソードで人間のテレオペレーションか自律制御かを選択し、人間のコストを最小化しながら模倣学習と強化学習で自律性能を向上させる手法を提案。
著者: Marc Rigter, Bruno Lacerda, Nick Hawes
分類: cs.LG, cs.RO
原文アブストラクト
We consider robot learning in the context of shared autonomy, where control of the system can switch between a human teleoperator and autonomous control. In this setting we address reinforcement learning, and learning from demonstration, where there is a cost associated with human time. This cost represents the human time required to teleoperate the robot, or recover the robot from failures. For each episode, the agent must choose between requesting human teleoperation, or using one of its autonomous controllers. In our approach, we learn to predict the success probability for each controller, given the initial state of an episode. This is used in a contextual multi-armed bandit algorithm to choose the controller for the episode. A controller is learnt online from demonstrations and reinforcement learning so that autonomous performance improves, and the system becomes less reliant on the teleoperator with more experience. We show that our approach to controller selection reduces the human cost to perform two simulated tasks and a single real-world task.
