论文标题
使用身体角色部门在各种配置的机器人上进行任务的运动映射
Task-oriented Motion Mapping on Robots of Various Configuration using Body Role Division
论文作者
论文摘要
许多在机器人教学中的工作仅着眼于教学任务知识，例如几何约束或运动知识，例如完成任务的运动。但是，要有效地向机器人讲一个复杂的任务序列，要利用任务和运动知识很重要。任务知识提供了序列中每个单独任务的目标，并减少了所需的人类演示的数量，而运动知识包含任务到任务的约束，否则该约束将需要专家知识来对问题进行建模。在本文中，我们提出了一种身体角色划分方法，该方法将两种类型的知识结合使用。该方法的灵感来自于人体运动的事实，并使用人体结构类比将机器人的身体构型分解为不同的角色：模仿人体运动和身体部位的主体部位，这些部位替代了对任务知识进行模仿的替代性。我们的结果表明，我们的方法缩放到不同数量的ARM链路的机器人，指导机器人的配置与实现即将完成任务的配置，并且可能对教授一系列任务序列有益。
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 paper, 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.
