论文标题
与混合现实学习机器人运动技能
Learning robot motor skills with mixed reality
论文作者
论文摘要
混合现实（MR）最近作为使最终用户能够教授机器人的直观界面表现出了巨大的成功。相关工作使用MR界面将机器人意图和信念传达给共同存在的人类，并开发了用于采用多模式人类输入和学习复杂运动行为的算法。即使取得了这些成功，使最终用户能够教授机器人复杂的运动任务仍然构成挑战，因为最终用户的交流是高度依赖性的，并且世界知识高度多样。我们提出了一个学习框架，最终用户教机器人a）运动演示，b）任务约束，c）计划表示形式和d）对象信息，所有这些信息都集成到基于动态运动基础（DMP）的单个运动技能学习框架中。我们假设传达这个世界知识将与MR接口保持直觉，并且具有各种世界知识模式的样本有效的运动技能学习框架将使机器人能够有效地解决复杂的任务。
Mixed Reality (MR) has recently shown great success as an intuitive interface for enabling end-users to teach robots. Related works have used MR interfaces to communicate robot intents and beliefs to a co-located human, as well as developed algorithms for taking multi-modal human input and learning complex motor behaviors. Even with these successes, enabling end-users to teach robots complex motor tasks still poses a challenge because end-user communication is highly task dependent and world knowledge is highly varied. We propose a learning framework where end-users teach robots a) motion demonstrations, b) task constraints, c) planning representations, and d) object information, all of which are integrated into a single motor skill learning framework based on Dynamic Movement Primitives (DMPs). We hypothesize that conveying this world knowledge will be intuitive with an MR interface, and that a sample-efficient motor skill learning framework which incorporates varied modalities of world knowledge will enable robots to effectively solve complex tasks.
