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arXiv · 2509.02815

Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization

Nico Bohlinger·Jan Peters
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Abstract

We present a single, general locomotion policy trained on a diverse collection of 50 legged robots. By combining an improved embodiment-aware architecture (URMAv2) with a performance-based curriculum for extreme Embodiment Randomization, our policy learns to control millions of morphological variations. Our policy achieves zero-shot transfer to unseen real-world humanoid and quadruped robots.

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Nico Bohlinger, Jan Peters. 2025-09-02. Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization. https://arxiv.org/abs/2509.02815

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