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About this paper
Nico Bohlinger, Jan Peters
Researchers developed URMAv2, an enhanced learning architecture combined with extreme embodiment randomization, to train a single locomotion policy capable of controlling 50 diverse legged robots simultaneously. This policy demonstrates improved learning efficiency and achieves zero-shot sim-to-real transfer to real-world quadrupeds and stable locomotion on a real humanoid.
Key insights
- Embodiment-aware architectures, when properly scaled and refined, are effective for learning general locomotion policies that can control a massively diverse set of robot morphologies.
- Combining extreme online embodiment randomization with a performance-based curriculum is crucial for stable and efficient training when dealing with a vast and varied embodiment space.
- Achieving stable locomotion on complex real-world humanoids with a single general policy is feasible, though robust zero-shot sim-to-real transfer for humanoids under all conditions remains an area for further research.
Summary by alphaXiv.
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