Using robust and simplified geometric models in skill-based manipulation

Manipulator tasks such as assembly and disassembly can generally be divided into several motion primitives. We call these "skills" and explain how most manipulator tasks can be composed of skill sequences. Skills are also used to compensate for errors both in the geometric model and in manipulator motions. There are dispensable data in the shapes, positions and orientations of objects when achieving skill motions in a task Therefore, we can derive simplified geometric models by considering both dispensable and indispensable data in a skill motion. We call such robust and simplified models "false models." This paper describes our definition of false models in planning and visual sensing and shows the effectiveness of our method using examples