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Supervised autonomy for online learning in human-robot interaction

机译：人机交互中在线学习的监督自主权

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When a robot is learning it needs to explore its environment and how its environment responds on its actions. When the environment is large and there are a large number of possible actions the robot can take, this exploration phase can take prohibitively long. However, exploration can often be optimised by letting a human expert guide the robot during its learning. Interactive machine learning, in which a human user interactively guides the robot as it learns, has been shown to be an effective way to teach a robot. It requires an intuitive control mechanism to allow the human expert to provide feedback on the robot's progress. This paper presents a novel method which combines Reinforcement Learning and Supervised Progressively Autonomous Robot Competencies (SPARC). By allowing the user to fully control the robot and by treating rewards as implicit, SPARC aims to learn an action policy while maintaining human supervisory oversight of the robot's behaviour. This method is evaluated and compared to Interactive Reinforcement Learning in a robot teaching task. Qualitative and quantitative results indicate that SPARC allows for safer and faster learning by the robot, whilst not placing a high workload on the human teacher. (C) 2017 Elsevier B.V. All rights reserved.
机译：当机器人正在学习时，它需要探索其环境以及其环境如何响应其动作。当环境很大且机器人可以执行许多可能的操作时，此探索阶段可能会花很长时间。但是，通常可以通过让人类专家在机器人学习期间引导机器人来优化探索。交互式机器学习已被证明是教机器人的有效方法，在交互式机器学习中，人类用户可以在学习过程中交互式地指导机器人。它需要直观的控制机制，以允许人类专家提供有关机器人进度的反馈。本文提出了一种结合强化学习和监督式渐进式自主机器人能力（SPARC）的新颖方法。通过允许用户完全控制机器人并通过将奖励视为隐性，SPARC旨在学习行动策略，同时保持对机器人行为的人为监督。该方法经过评估，并与机器人教学任务中的交互式强化学习进行了比较。定性和定量的结果表明，SPARC可以使机器人更安全，更快速地学习，而不会给人类教师带来繁重的工作量。 （C）2017 Elsevier B.V.保留所有权利。

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