Online Discovery of Locomotion Modes for Wheel-Legged Hybrid Robots: a Transferability-based Approach

Abstract : Wheel-legged hybrid robots promise to combine the e ciency 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. Al- though di erent 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 discov- ers 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 re ects how well the behavior of the robot is in agreement with the pre- dictions of the forward model. This transferability function is approximated by conducting 20 experiments of one second on the real robot during the op- timization. In the three investigated situations ( at ground, grass-like terrain, tunnel-like environment), our method found e cient controllers for forward locomotion in 1 to 2 minutes: the robot used its wheels on the at ground, it walked on the grass-like terrain and moved with a lowered body in the tunnel- like environment.
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Sylvain Koos, Jean-Baptiste Mouret. Online Discovery of Locomotion Modes for Wheel-Legged Hybrid Robots: a Transferability-based Approach. 14th International Conference on Climbing and Walking Robots, Sep 2011, Paris, France. pp.1. ⟨hal-00633930⟩

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