Presentado al ICAR 2011 celebrado en Tallin (Estonia) del 20 al 23 de junio de 2011. ; A learning framework with a bidirectional communication channel is proposed, where a human performs several demonstrations of a task using a haptic device (providing him/her with force-torque feedback) while a robot captures these executions using only its force-based perceptive system. Our work departs from the usual approaches to learning by demonstration in that the robot has to execute the task blindly, relying only on force-torque perceptions, and, more essential, we address goal-driven manipulation tasks with multiple solution trajectories, whereas most works tackle tasks that can be learned by just finding a generalization at the trajectory level. To cope with these multiple-solution tasks, in our framework demonstrations are represented by means of a Hidden Markov Model (HMM) and the robot reproduction of the task is performed using a modified version of Gaussian Mixture Regression that incorporates temporal information (GMRa) through the forward variable of the HMM. Also, we exploit the haptic device as a teaching and communication tool in a human-robot interaction context, as an alternative to kinesthetic-based teaching systems. Results show that the robot is able to learn a container-emptying task relying only on force-based perceptions and to achieve the goal from several non-trained initial conditions. ; This research is partially sponsored by the European projects GARNICS (FP7-247947) and IntellAct (FP7-269959), the Spanish projects Multimodal Interaction in Pattern Recognition and Computer Vision (MIPRCV) (Consolider Ingenio 2010 project CSD2007-00018) and PAU (DPI2008-06022). L. Rozo was supported by the CSIC under a JAE-PREDOC scholarship ; Peer reviewed


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    Titel :

    Robot learning from demonstration of force-based tasks with multiple solution trajectories


    Beteiligte:

    Erscheinungsdatum :

    01.01.2011



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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