Trabajo presentado al IROS celebrado en Vilamoura (Portugal) del 7 al 12 de octubre de 2012. ; Rigid object manipulation with robots has mainly relied on precise, expensive models and deterministic sequences. Given the great complexity of accurately modeling deformable objects, their manipulation seems to call for a rather different approach. This paper proposes a probabilistic planner, based on a Partially Observable Markov Decision Process (POMDP), targeted at reducing the inherent uncertainty of deformable object sorting. It is shown that a small set of unreliable actions and inaccurate perceptions suffices to accomplish the task, provided faithful statistics on both of them are collected beforehand. The planner has been applied to a clothes sorting task in a real case context with a depth and color sensor and a robotic arm. Experimental results show the promise of the approach since more than 95% certainty of having isolated a piece of clothing is reached in an average of four steps for quite entangled initial clothing configurations. ; This work has been partially funded by Spanish Ministry of Economy and Competitiveness under projects PAU+ DPI2011-27510 and the Catalan Research Commission 2009SGR155; and by the EU project IntellAct FP7-269959. ; Peer Reviewed


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    Title :

    POMDP approach to robotized clothes separation


    Contributors:

    Publication date :

    2012-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



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