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asRoBallet: Dynamic Balance Control of Spherical Underactuated Robots via Friction-Aware Reinforcement Learning

asRoBallet achieves dynamic balance control of spherical underactuated robots through friction-aware reinforcement learning. Starting from underactuated dynamics and the two-layer contact friction among wheel, ball, and ground, it derives the kinematic mapping and dynamic balance mechanism of the spherical chassis. The method uses proximal policy optimization (PPO) to train position-holding and velocity-tracking policies, and applies friction domain randomization to improve policy adaptability under different friction conditions.

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