论文标题
学习行动的持续时间和协同作用在人机合作的任务计划中
Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration
论文作者
论文摘要
对动作成本的良好估计是人类机器人协作的任务计划的关键。动作的持续时间取决于代理的能力以及人类和机器人同时执行的动作之间的相关性。本文提出了一种学习行动成本的方法，并在人类和机器人同时执行的动作之间进行耦合。我们利用过去执行的信息来了解每个动作的平均持续时间和协同系数，代表了人类对机器人执行的动作持续时间（反之亦然）执行的动作的效果。我们在模拟方案中实现了建议的方法，在该方案中，这两个代理都可以同时访问同一区域。安全措施要求机器人在人类接近时放慢脚步，表示在同一区域运行的任务的不良协同作用。我们表明我们的方法可以学习如此糟糕的耦合，以便任务计划者可以利用此信息来找到更好的计划。
A good estimation of the actions' cost is key in task planning for human-robot collaboration. The duration of an action depends on agents' capabilities and the correlation between actions performed simultaneously by the human and the robot. This paper proposes an approach to learning actions' costs and coupling between actions executed concurrently by humans and robots. We leverage the information from past executions to learn the average duration of each action and a synergy coefficient representing the effect of an action performed by the human on the duration of the action performed by the robot (and vice versa). We implement the proposed method in a simulated scenario where both agents can access the same area simultaneously. Safety measures require the robot to slow down when the human is close, denoting a bad synergy of tasks operating in the same area. We show that our approach can learn such bad couplings so that a task planner can leverage this information to find better plans.
