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.
One policy to run them all: an end-to-end learning approach to multi-embodiment locomotion
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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.