Acquisition and modification of motion knowledge using continuous HMMs for motion imitation of humanoids

Abstract

A knowledge-based approach to imitation learning of motion generation for humanoid robots and an imitative motion generation system based on motion knowledge learning and reuse are described. The system has three parts: recognizing, learning, and modifying parts. The first part recognizes an instructed motion distinguishing it from the motion knowledge database by the continuous hidden Markov model. When the motion is recognized as being unfamiliar, the second part learns it using locally weighted regression and acquires a knowledge of the motion. When a robot recognizes the instructed motion as familiar or judges that its acquired knowledge is applicable to the motion generation, the third part imitates the instructed motion by modifying a learned motion. This paper reports some performance results: the motion imitation of several radio gymnastics motions.