Efficient behavior learning in human–robot collaboration
Efficient behavior learning in human–robot collaboration
复制标题
人机协作中的高效行为学习
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Lopes
中科院分区：
文献类型：
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作者：
Thibaut Munzer;Marc Toussaint;M. Lopes
我们提出了一种新颖的方法，让机器人在执行人机联合任务时进行交互学习。我们考虑由人类操作员和适应人类任务执行偏好的机器人助手组成的团队实现的协作任务。不同的操作人员可能具有不同的能力、经验和个人偏好，因此团队中的特定活动分配比其他活动更受青睐。我们的主要目标是让机器人学习任务和用户的偏好，以提供更有效和可接受的联合任务执行。我们将并发多智能体协作描述为一个半马尔可夫决策过程，并展示了如何建模团队行为和学习预期机器人行为。我们进一步提出了一个交互式学习框架，并在模拟和真实机器人设置中对其进行了评估，以表明系统可以有效地学习并适应人类的期望。
We present a novel method for a robot to interactively learn, while executing, a joint human–robot task. We consider collaborative tasks realized by a team of a human operator and a robot helper that adapts to the human’s task execution preferences. Different human operators can have different abilities, experiences, and personal preferences so that a particular allocation of activities in the team is preferred over another. Our main goal is to have the robot learn the task and the preferences of the user to provide a more efficient and acceptable joint task execution. We cast concurrent multi-agent collaboration as a semi-Markov decision process and show how to model the team behavior and learn the expected robot behavior. We further propose an interactive learning framework and we evaluate it both in simulation and on a real robotic setup to show the system can effectively learn and adapt to human expectations.