Learning in-contact control strategies from demonstration
Mattia Racca, Joni Pajarinen, Alberto Montebelli, Ville Kyrki
Abstract
Learning to perform tasks like pulling a door handle or pushing a button, inherently easy for a human, can be surprisingly difficult for a robot. A crucial problem in these kinds of in-contact tasks is the context specificity of pose and force requirements. In this paper, a robot learns in-contact tasks from human kinesthetic demonstrations. To address the need to balance between the position and force constraints, we propose a model based on the hidden semi-Markov model (HSMM) and Cartesian impedance control. The model captures uncertainty over time and space and allows the robot to smoothly satisfy a task's position and force constraints by online modulation of impedance controller stiffness according to the HSMM state belief. In experiments, a KUKA LWR 4+ robotic arm equipped with a force/torque sensor at the wrist successfully learns from human demonstrations how to pull a door handle and push a button.
BibTeX
@inproceedings{iros2016_learningincontac,
title = {Learning in-contact control strategies from demonstration},
author = {Mattia Racca and Joni Pajarinen and Alberto Montebelli and Ville Kyrki},
booktitle = {IROS 2016},
year = {2016}
}