Wheel-legged hybrid robots promise to combine the efficiency of wheeled robots with the versatility of legged robots: they are able to roll on simple terrains, to dynamically adapt their posture and even to walk on uneven grounds. Although different locomotion modes of such robots have been studied, a pivotal question remains: how to automatically adapt the locomotion mode when the environment changes? We here propose that the robot autonomously discovers its locomotion mode using optimization-based learning. To that aim, we introduce a new algorithm that relies on a forward model and a stochastic multi-objective optimization. Three objectives are optimized: (1) the average displacement speed, (2) the expended energy and (3) the transferability score, which reflects how well the behavior of the robot is in agreement with the predictions of the forward model. This transferability function is approximated by conducting 20 experiments of one second on the real robot during the optimization. In the three investigated situations (flat ground, grass-like terrain, tunnel-like environment), our method found efficient controllers for forward locomotion in 1 to 2 minutes: the robot used its wheels on the flat ground, it walked on the grass-like terrain and moved with a lowered body in the tunnel-like environment.


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    Title :

    Online discovery of locomotion modes for wheel-legged hybrid robots: A transferability-based approach


    Contributors:
    Koos, S. (author) / Mouret, J.B. (author)


    Publication date :

    2011


    Size :

    8 Seiten, 4 Bilder, 1 Tabelle, 12 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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