Abstract:Control of wheeled humanoid locomotion is a challenging problem due to the nonlinear dynamics and underactuated characteristics of these robots. Traditionally, feedback controllers have been utilized for stabilization and locomotion. However, these methods are often limited by the fidelity of the underlying model used, choice of controller, and environmental variables considered (surface type, ground inclination, etc). Recent advances in reinforcement learning (RL) offer promising methods to tackle some of the… Show more
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