Task-oriented Motion Mapping on Robots of Various Configuration using Body Role Division
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Many works in robot teaching either focus on teaching a high-level abstract knowledge such as task constraints, or low-level concrete knowledge such as the motion for accomplishing a task. However, we show that both high-level and low-level knowledge is required for teaching a complex task sequence such as opening and holding a fridge with one arm while reaching inside with the other. In this paper, we propose a body role division approach, which maps both high-level task goals and low-level motion obtained through human demonstration, to robots of various configurations. 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 achieving a demonstrated motion, and body parts that are substitutional for adjusting the motion to achieve an instructed task goal. Our results show that our method scales to robots of different number of arm links, and that both high and low level knowledge is mapped to achieve a multi-step dual arm manipulation task. In addition, our results indicate that when either the high or low level knowledge of the task is missing, or when mapping is done without the role division, a robot fails to open a fridge door or is not able to navigate its footprint appropriately for an upcoming task. We show that such results not only apply to human-shaped robots with two link arms, but to robots with less degrees of freedom such as a one link armed robot.
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