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Generate, Track, Improve: Perceptive Multi-Skill Humanoid Locomotion with RL-Fine-Tuned Motion Generators — arXiv:2609.31577

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31577
License (as archived): CC0-1.0
Archived: 2026-10-08T03:15:07.135Z
SHA-256 of the archived text: 048b3462bc6692826f9fcc85ddfad475520013ff89c554f06b587b67dba49ac2
Stable URL of this page: https://metraipolis.com/documento/dc_61caee34fbe2

How to cite

Generate, Track, Improve: Perceptive Multi-Skill Humanoid Locomotion with RL-Fine-Tuned Motion Generators — arXiv:2609.31577 — arXiv (en), https://arxiv.org/abs/2609.31577 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_61caee34fbe2 (Metraipolis archive dc_61caee34fbe2, sha256 048b3462bc6692826f9fcc85ddfad475520013ff89c554f06b587b67dba49ac2)

BibTeX

@misc{dc61caee34fbe2,
  title = {Generate, Track, Improve: Perceptive Multi-Skill Humanoid Locomotion with RL-Fine-Tuned Motion Generators — arXiv:2609.31577},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31577},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_61caee34fbe2; sha256 048b3462bc6692826f9fcc85ddfad475520013ff89c554f06b587b67dba49ac2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31577. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

General purpose humanoids require locomotion controllers that are multi-skill, perceptive, dynamic, and robust enough to go anywhere humans can. In this work, we present a two layer locomotion architecture: (1) a perceptive flow matching motion generator plans whole body trajectories from raw depth images while a (2) perceptive tracking policy trained with control-guided RL follows these motions. Both policies are trained on a library of terrain consistent motion clips created with dynamically optimized human data which yields both accurate velocity tracking and terrain consistent references. Our central contribution is a simple yet effective off-policy RL fine tuning loop that improves the motion generator. A structured search method is used with the generator to gather data for advantage weighted regression. This off-policy loop is much more sample efficient than on-policy residual fine tuning and improves terrain consistency on unseen geometries and skill compositions. We find that successful terrain traversals increased by up to 25 percentage points and skill selection improved by up to 80 percentage points. By using raw depth images to perceive the environment no odometry or height maps are needed, and outdoor deployment is easy. With two cameras, the policy can see terrain coming from further away and adjust its velocity regardless of the commanded speed so it can traverse the terrain. A single policy pair enables a Unitree G1 humanoid to walk, run, stand, jump on and off of boxes, and traverse stairs in outdoor environments. Project page: this https URL

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-28 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.31577 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T02:58:09.019Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).