Pardowitz M, Haschke R, Steil JJ, Ritter H. Gestalt-Based Action Segmentation for Robot Task Learning. In: IEEE-RAS 7th International Conference on Humanoid Robots (HUMANOIDS) . 2008: 347-352. ; In Programming by Demonstration (PbD) systems, the problem of task segmentation and task decomposition has not been addressed with satisfactory attention. In this article we propose a method relying on psychological gestalt theories originally developed for visual perception and apply it to the domain of action segmentation. We propose a computational model for gestalt-based segmentation called Competitive Layer Model (CLM). The CLM relies on features mutually supporting or inhibiting each other to form segments by competition. We analyze how gestalt laws for actions can be learned from human demonstrations and how they can be beneficial to the CLM segmentation method. We validate our approach with two reported experiments on action sequences and present the results obtained from those experiments


    Access

    Download


    Export, share and cite



    Title :

    Gestalt-Based Action Segmentation for Robot Task Learning


    Contributors:

    Publication date :

    2008-01-01


    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    004 / 629




    Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration

    Samuele Sandrini / Marco Faroni / Nicola Pedrocchi | BASE | 2022

    Free access


    Gestalt-Guided Image Understanding for Few-Shot Learning

    Song, Kun / Wu, Yuchen / Chen, Jiansheng et al. | British Library Conference Proceedings | 2023


    Robot Task Learning from Human Demonstration

    Ekvall, Staffan | BASE | 2007

    Free access

    Simple gestalt algebra

    Michaelsen, E. / Yashina, V. V. | British Library Online Contents | 2014