Human-Centered Collaborative Robots With Deep Reinforcement Learning

摘要

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 time-efficient 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. Two important benefits of the proposed approach are that tedious annotation of motion data is avoided, and the learning is performed on-line.