论文标题
首先学习任务或首先学习人类伴侣：人类机器人合作的分层任务分解方法
Learn Task First or Learn Human Partner First: A Hierarchical Task Decomposition Method for Human-Robot Cooperation
论文作者
论文摘要
在动态控制问题中，将深入的强化学习（DRL）应用于人类机器人合作（HRC），这是有希望的，但由于机器人需要学习受控系统的动态和人类伴侣的动态，这是充满挑战的。在现有的研究中，由DRL提供支持的机器人采用对环境的耦合观察和人类伴侣同时学习这两种动态。但是，这种学习策略在学习效率和团队绩效方面受到限制。这项工作提出了一种新颖的任务分解方法，它具有层次奖励机制，使机器人能够从学习人类伴侣的行为中分别学习层次动态控制任务。该方法通过人类主题实验在模拟环境中通过层次控制任务进行验证。我们的方法还提供了有关HRC学习策略设计的洞察力。结果表明，机器人应该首先学习任务以实现更高的团队绩效，并首先学习人类以实现更高的学习效率。
Applying Deep Reinforcement Learning (DRL) to Human-Robot Cooperation (HRC) in dynamic control problems is promising yet challenging as the robot needs to learn the dynamics of the controlled system and dynamics of the human partner. In existing research, the robot powered by DRL adopts coupled observation of the environment and the human partner to learn both dynamics simultaneously. However, such a learning strategy is limited in terms of learning efficiency and team performance. This work proposes a novel task decomposition method with a hierarchical reward mechanism that enables the robot to learn the hierarchical dynamic control task separately from learning the human partner's behavior. The method is validated with a hierarchical control task in a simulated environment with human subject experiments. Our method also provides insight into the design of the learning strategy for HRC. The results show that the robot should learn the task first to achieve higher team performance and learn the human first to achieve higher learning efficiency.
