A reinforcement learning method lets legged robots jointly learn information-seeking actions and predict joint-level and global embodiment parameters using a history-augmented URMA model in simulation.
Domain randomization for transferring deep neural networks from simulation to the real world
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Active Embodiment Identification with Reinforcement Learning for Legged Robots
A reinforcement learning method lets legged robots jointly learn information-seeking actions and predict joint-level and global embodiment parameters using a history-augmented URMA model in simulation.