Incremental motion primitive learning by physical coaching using impedance control

Abstract

We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method allows for iterative execution and motion refinement using a forgetting factor. During the iterative motion refinement, a confidence value specifies an area of allowed refinement around the nominal trajectory. A novel method for continuous generation of motions from a hidden Markov model (HMM) representation of motion primitives is proposed, which incorporates relative time information for each state. On the real-time control level, the kinesthetic teaching is handled by a customized impedance controller, which combines tracking performance with soft physical interaction and allows to implement soft boundaries for the motion refinement. The proposed methods were implemented and tested using DLR's humanoid upper-body robot Justin.