Many works in robot teaching either focus only on teaching task knowledge, such as geometric constraints, or motion knowledge, such as the motion for accomplishing a task. However, to effectively teach a complex task sequence to a robot, it is important to take advantage of both task and motion knowledge. The task knowledge provides the goals of each individual task within the sequence and reduces the number of required human demonstrations, whereas the motion knowledge contain the task-to-task constraints that would otherwise require expert knowledge to model the problem. In this letter, we propose a body role division approach that combines both types of knowledge using a single human demonstration. The method is inspired by facts on human body motion and uses a body structural analogy to decompose a robot’s body configuration into different roles: body parts that are dominant for imitating the human motion and body parts that are substitutional for adjusting the imitation with respect to the task knowledge. Our results show that our method scales to robots of different number of arm links, guides a robot’s configuration to one that achieves an upcoming task, and is potentially beneficial for teaching a range of task sequences.

中文翻译：

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使用身体角色划分的各种配置机器人的面向任务的运动映射


机器人教学中的许多工作要么只关注任务知识的教学，例如几何约束，要么只关注运动知识，例如完成任务的运动。然而，为了有效地向机器人教授复杂的任务序列，利用任务和运动知识非常重要。任务知识提供了序列中每个单独任务的目标，并减少了所需的人工演示的数量，而运动知识包含任务到任务的约束，否则需要专家知识来对问题进行建模。在这封信中，我们提出了一种身体角色划分方法，该方法使用单个人类演示将两种类型的知识结合起来。该方法受到人体运动事实的启发，利用身体结构类比将机器人的身体配置分解为不同的角色：对于模仿人体运动起主导作用的身体部位和对于调整模仿相对于人体运动具有替代性的身体部位。任务知识。我们的结果表明，我们的方法可以扩展到不同数量臂连杆的机器人，指导机器人的配置以实现即将到来的任务，并且可能有利于教授一系列任务序列。