Title :
Incremental motion primitive learning by physical coaching using impedance control
Author :
Lee, Dongheui ; Ott, Christian
Author_Institution :
Dept. of Electr. Eng. & Inf. Technol., Tech. Univ. of Munich, Munich, Germany
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.
Keywords :
hidden Markov models; humanoid robots; iterative methods; learning (artificial intelligence); motion control; hidden Markov model; humanoid robot; impedance control; incremental motion; iterative motion refinement; kinesthetic teaching; primitive learning;
Conference_Titel :
Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-6674-0
DOI :
10.1109/IROS.2010.5650519