A single reinforcement learning policy jointly trains multiple locomotion skills for wheeled-legged robots with DC-motor constraints and learns a proprioceptive skill selector for adaptive behavior.
Versatile skill control via self-supervised adversarial imitation of unlabeled mixed motions,
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MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots
A single reinforcement learning policy jointly trains multiple locomotion skills for wheeled-legged robots with DC-motor constraints and learns a proprioceptive skill selector for adaptive behavior.