深層強化学習による人間中心の協働ロボット
Human-centered collaborative robots with deep reinforcement learning
人間とロボットの協働作業を、知覚と意思決定を統合した教師なしの深層強化学習で end-to-end に学習し、注釈なしで新しい相手やタスクに素早く適応できる枠組みを提案した論文。
著者: Ali Ghadirzadeh, Xi Chen, Wenjie Yin, Zhengrong Yi, Mårten Björkman, Danica Kragic
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
We present a reinforcement learning based framework for human-centered collaborative systems. The framework is proactive and balances the benefits of timely actions with the risk of taking improper actions by minimizing the total time spent to complete the task. The framework is learned end-to-end in an unsupervised fashion addressing the perception uncertainties and decision making in an integrated manner. The framework is shown to provide more fluent coordination between human and robot partners on an example task of packaging compared to alternatives for which perception and decision-making systems are learned independently, using supervised learning. The foremost benefit of the proposed approach is that it allows for fast adaptation to new human partners and tasks since tedious annotation of motion data is avoided and the learning is performed on-line.
