Programming-by-Demonstration and Adaptation of Robot Skills by Fuzzy Time Modeling

Abstract

Robot skills are motion or grasping primitives from which a complicated robot task consists. Skills can be directly learned and recognized by a technique named programming-by-demonstration. A human operator demonstrates a set of reference skills where his motions are recorded by a data-capturing system and modeled via fuzzy clustering and a Takagi–Sugeno modeling technique. The skill models use time instants as input and operator actions as outputs. In the recognition phase, the robot identifies the skill shown by the operator in a novel test demonstration. Finally, using the corresponding reference skill model the robot executes the recognized skill. Skill models can be updated online where drastic differences between learned and real world conditions are eliminated using the Broyden update formula. This method was extended for fuzzy models especially for time cluster models.