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.

中文翻译：

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具有深度强化学习的以人为本的协作机器人

我们为以人为中心的协作系统提出了一个基于强化学习的框架。该框架是主动的，通过最大限度地减少完成任务所花费的总时间，在及时行动的好处与采取不当行动的风险之间取得平衡。该框架以无监督的方式端到端学习，以综合方式解决感知不确定性和决策制定。与使用监督学习独立学习感知和决策系统的替代方案相比，该框架在包装示例任务上提供了人类和机器人合作伙伴之间更流畅的协调。