<ul><li>Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion.</li><li>URMA (Unified Robot Morphology Architecture) is introduced as a framework to control different embodiments of legged robots.</li><li>The framework utilizes an end-to-end Multi-Task Reinforcement Learning approach and morphology-agnostic encoders and decoders.</li><li>Experiments show that URMA can learn a locomotion policy that can be transferred to unseen robot platforms in simulation and the real world.</li></ul>

One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion

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