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

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. Al-though 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 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 reflects 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 (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