Learning in-contact control strategies from demonstration

Mattia Racca, Joni Pajarinen, Alberto Montebelli, Ville Kyrki

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

31 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Number of pages8
ISBN (Electronic)9781509037629
Publication statusPublished - 28 Nov 2016
MoE publication typeA4 Article in a conference publication
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - Daejeon, Korea, Republic of
Duration: 9 Oct 201614 Oct 2016

Publication series

NameProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
Country/TerritoryKorea, Republic of
Internet address


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