Wiping behavior is a task of tracing the surface of an object while feeling the force with the palm of the hand. It is necessary to adjust the force and posture appropriately considering the various contact conditions felt by the hand. Several studies have been conducted on the wiping motion, however, these studies have only dealt with a single surface material, and have only considered the application of the amount of appropriate force, lacking intelligent movements to ensure that the force is applied either evenly to the entire surface or to a certain area. Depending on the surface material, the hand posture and pressing force should be varied appropriately, and this is highly dependent on the definition of the task. Also, most of the movements are executed by high-rigidity robots that are easy to model, and few movements are executed by robots that are low-rigidity but therefore have a small risk of damage due to excessive contact. So, in this study, we develop a method of motion generation based on the learned prediction of contact force during the wiping motion of a low-rigidity robot. We show that MyCobot, which is made of low-rigidity resin, can appropriately perform wiping behaviors on a plane with multiple surface materials based on various task definitions.

翻译：擦拭行为是一项用手掌感知接触力同时沿物体表面轨迹运动的任务。需要根据手部感受到的不同接触条件，适当调整施力大小与姿态。目前已有若干关于擦拭运动的研究，但这些研究仅针对单一表面材料，且只考虑了施加适当力度的应用，缺乏确保力均匀作用于整个表面或特定区域的智能动作。根据表面材料的不同，手部姿态与按压压力应相应变化，且这种变化高度依赖于任务定义。此外，多数运动由易于建模的高刚度机器人执行，而低刚度机器人虽因接触过度导致损坏的风险较小，但相关运动研究较少。因此，本研究开发了一种基于接触力预测学习的运动生成方法，用于低刚度机器人的擦拭动作。我们证明，由低刚度树脂制成的MyCobot机器人能够根据多种任务定义，在包含多种表面材料的平面上适当地执行擦拭行为。