Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915477101477888 |
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| author | Bohlinger, Nico Peters, Jan |
| author_facet | Bohlinger, Nico Peters, Jan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02815 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization Bohlinger, Nico Peters, Jan Robotics Machine Learning 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. |
| title | Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2509.02815 |