Recognition and Teaching of Robot Skills by Fuzzy Time-Modeling

Abstract

Robot skills are low-level motion and/or grasping ca- pabilities that constitute the basic building blocks from which tasks are built. Teaching and recognition of such skills can be done by Programming-by-Demonstration approach. A human operator demonstrates certain skills while his motions are recorded by a data- capturing device and modeled in our case via fuzzy clustering and Takagi-Sugeno modeling technique. The resulting skill models use the time as input and the operator's actions and reactions as outputs. Given a test skill by the human operator the robot control system recognizes the individual phases of skills and generates the type of skill shown by the operator.