E-034

An Observation Model and Segmentation Algorithm for Skill Acquisition of a Deburring Task

Authors: E. Aertbelien, H. Van Brussel
Affiliation: Katholieke Universiteit Leuven

Abstract
In robotic deburring applications it is desirable to have sensor feedback. The control strategies for this sensor feedback have to be adapted frequently to work piece and work tool parameters. This paper discusses a method for transferring the skill of a human operator to a control strategy that can cope with these changes in parameters. The human skill is transferred by an indirect learning method. The human actions are modeled as an impedance controller whose parameters are adapted by observations of the deburring process state. The non-linear relation between the process state and the controller parameters is learnt by a neural network. To apply this method, it is necessary that the observations are independent of the controller actions. This is shown for an observation model that is derived from a process model for deburring and experimentally verified. Segmentation of the training data is done by analyzing the summed normalized innovation squared value (SNIS) of a static Kalman filter.

Katholieke Universiteit Leuven
Department of Mechanical Engineering
Division PMA
Celestijnenlaan 300B
B-3001 HEVERLEE Belgium
Phone: +32-16-32 25 14
Fax +32-16-32 29 87
Erwin.Aertbelien@mech.kuleuven.ac.be