Probabilistic decomposition of sequential force interaction tasks into movement primitives

Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    4 Citations (Scopus)

    Abstract

    Learning sequential force interaction tasks from kinesthetic demonstrations is a promising approach to transfer human manipulation abilities to a robot. In this paper we propose a novel concept to decompose such demonstrations into a set of Movement Primitives (MPs). The decomposition is based on a probability distribution we call Directional Normal Distribution (DND). To capture the sequential properties of the manipulation task, we model the demonstrations with a Hidden Markov Model (HMM). Here, we employ mixtures of DNDs as the HMM's output emissions. The combination of HMMs and mixtures of DNDs allows to infer the MP's composition, i.e., its coordinate frames, control variables and target coordinates from the demonstration data. In addition, it permits to determine an appropriate number of MPs that explains the demonstrations best. We evaluate the approach on kinesthetic demonstrations of a light bulb unscrewing task. Decomposing the task leads to intuitive and meaningful MPs that reflect the natural structure of the task.

    Original languageEnglish
    Title of host publicationProceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
    Subtitle of host publicationIROS 2016
    EditorsDong-Soo Kwon, Chul-Goo Kang, Il Hong Suh
    Place of PublicationPiscataway, NJ, USA
    PublisherIEEE
    Pages3920-3927
    ISBN (Electronic)978-1-5090-3762-9
    DOIs
    Publication statusPublished - 2016
    Event2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 - Daejeon, Korea, Republic of
    Duration: 9 Oct 201614 Oct 2016
    http://www.iros2016.org/

    Conference

    Conference2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
    Abbreviated titleIROS 2016
    CountryKorea, Republic of
    CityDaejeon
    Period9/10/1614/10/16
    Internet address

    Keywords

    • Hidden Markov models
    • Force
    • Robot kinematics
    • Gaussian distribution
    • Force measurement
    • Probabilistic logic

    Fingerprint

    Dive into the research topics of 'Probabilistic decomposition of sequential force interaction tasks into movement primitives'. Together they form a unique fingerprint.

    Cite this