Robotic Playing for Hierarchical Complex Skill Learning

Sandor Szedmak, Simon Hangle, Emre Ugur, Justus Piater

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

Abstract

In complex manipulation scenarios (e.g. tasks requiring complex interaction of two hands or in-hand manipulation), generalization is a hard problem. Current methods still either require a substantial amount of (supervised) training data and / or strong assumptions on both the environment and the task. In this paradigm, controllers solving these tasks tend to be complex. We propose a paradigm of maintaining simpler controllers solving the task in a small number of specific situations. In order to generalize to novel situations, the robot transforms the environment from novel situations into a situation where the solution of the task is already known. Our solution to this problem is to play with objects and use previously trained skills (basis skills). These skills can either be used for estimating or for changing the current state of the environment and are organized in skill hierarchies. The approach is evaluated in complex pick-and-place scenarios that involve complex manipulation. We further show that these skills can be learned by autonomous playing.
Original languageEnglish
Title of host publication2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages2799-2804
Number of pages6
DOIs
Publication statusPublished - 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
http://www.iros2016.org
http://www.iros2016.org/

Publication series

NameNimeke Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherIEEE
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
CountryKorea, Republic of
CityDaejeon
Period09/10/201614/10/2016
Internet address

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  • Cite this

    Szedmak, S., Hangle, S., Ugur, E., & Piater, J. (2016). Robotic Playing for Hierarchical Complex Skill Learning. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2799-2804). (Nimeke Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems). IEEE. https://doi.org/10.1109/IROS.2016.7759434