Using simplified geometric models in skill-based manipulation

Abstract

Dexterous manipulation plays an important role in working robots. 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 simplify geometric models by considering the dispensable data in a skill motion. We call such robust and simplified models 'false models'. This paper describes our definition of false models used in planning and visual sensing, and shows the effectiveness of our method using examples of tasks involving the manipulation of mechanical and electronic parts. Furthermore, we show the application of false models to objects of indefinite sizes and shapes using examples of the same tasks.