Joining High-Level Symbolic Planning with Low-Level Motion Primitives in Adaptive HRI: Application to Dressing Assistance
Joining High-Level Symbolic Planning with Low-Level Motion Primitives in Adaptive HRI: Application to Dressing Assistance
复制标题
在自适应 HRI 中将高级符号规划与低级运动原语相结合：在穿衣辅助中的应用
DOI：
10.1109/icra.2018.8460606
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Torras
中科院分区：
文献类型：
--
作者：
Gerard Canal;Emmanuel Pignat;G. Alenyà;S. Calinon;C. Torras
为了实现安全和成功的日常生活辅助，远离工厂的高度控制环境，机器人应该能够适应不断变化的情况。为这样的机器人编程是一个繁琐的过程，需要专业知识。另一种方法是依赖高级规划器，但是所使用的通用符号表示不太适合特定的机器人执行。相反，运动原语以一种容易适应不同情况的方式对机器人运动进行编码。本文提出了一个利用这两种方法的优点的组合框架。所需的符号状态的数量减少了，因为运动原语提供了“智能动作”，可以获取当前状态并在线处理变化。符号行为可以包括难以演示的交互（例如，询问和通知）。我们表明，所提出的框架可以适应用户的偏好（在机器人速度和机器人冗长方面），可以根据用户的运动重新调整轨迹，并且可以处理不可预见的情况。实验是在一个穿鞋的场景中进行的。这个场景特别有趣，因为它涉及到足够数量的操作，并且人机交互需要处理用户偏好和意外反应。
For a safe and successful daily living assistance, far from the highly controlled environment of a factory, robots should be able to adapt to ever-changing situations. Programming such a robot is a tedious process that requires expert knowledge. An alternative is to rely on a high-level planner, but the generic symbolic representations used are not well suited to particular robot executions. Contrarily, motion primitives encode robot motions in a way that can be easily adapted to different situations. This paper presents a combined framework that exploits the advantages of both approaches. The number of required symbolic states is reduced, as motion primitives provide “smart actions” that take the current state and cope online with variations. Symbolic actions can include interactions (e.g., ask and inform) that are difficult to demonstrate. We show that the proposed framework can adapt to the user preferences (in terms of robot speed and robot verbosity), can readjust the trajectories based on the user movements, and can handle unforeseen situations. Experiments are performed in a shoe-dressing scenario. This scenario is particularly interesting because it involves a sufficient number of actions, and the human-robot interaction requires the handling of user preferences and unexpected reactions.