Learning in-contact control strategies from demonstration
2016 (English)In: IROS 2016: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2016, 688-695 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE, 2016. 688-695 p.
Proceedings of the International Conference on Intelligent Robots and Systems, ISSN 2153-0858
IdentifiersURN: urn:nbn:se:his:diva-13319DOI: 10.1109/IROS.2016.7759127ISI: 000391921700101ScopusID: 2-s2.0-85006511822ISBN: 978-1-5090-3762-9 (electronic)OAI: oai:DiVA.org:his-13319DiVA: diva2:1066449
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Daejeon, South Korea, 9-14 October, 2016