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Nexus-VLA: Bridging Human and Robot Experiences via a Shared Hierarchical Semantic Latent Space — arXiv:2610.42021

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.42021
License (as archived): CC0-1.0
Archived: 2026-11-03T08:23:46.429Z
SHA-256 of the archived text: 812c68b16eaf29ffd1c3bb4d4a24852035940bb25e149781db6b110e31e0e221
Stable URL of this page: https://metraipolis.com/documento/dc_952800a46c4c

How to cite

Nexus-VLA: Bridging Human and Robot Experiences via a Shared Hierarchical Semantic Latent Space — arXiv:2610.42021 — arXiv (en), https://arxiv.org/abs/2610.42021 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_952800a46c4c (Metraipolis archive dc_952800a46c4c, sha256 812c68b16eaf29ffd1c3bb4d4a24852035940bb25e149781db6b110e31e0e221)

BibTeX

@misc{dc952800a46c4c,
  title = {Nexus-VLA: Bridging Human and Robot Experiences via a Shared Hierarchical Semantic Latent Space — arXiv:2610.42021},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.42021},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_952800a46c4c; sha256 812c68b16eaf29ffd1c3bb4d4a24852035940bb25e149781db6b110e31e0e221},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.42021. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large-scale egocentric human videos provide rich and scalable manipulation experience for robot policy learning, yet substantial embodiment gaps in visual appearance, kinematic structure, and action spaces hinder effective knowledge transfer. Existing methods often decouple cross-embodiment knowledge alignment from robot policy learning, making it difficult for human manipulation knowledge to directly and fully contribute to robot action learning. To address this issue, we propose Nexus-VLA, a hybrid policy learning framework that enables cross-embodiment knowledge transfer through end-to-end human-robot joint training, allowing human manipulation experience to directly contribute to robot policy learning. We first introduce a hierarchical cross-embodiment semantic alignment paradigm that progressively establishes shared manipulation semantics from task intent to fine-grained interactions, enabling more complete use of the manipulation knowledge contained in human videos. Building on this hierarchy, we design a shared manipulation latent representation that jointly encodes multi-level shared knowledge into an internal representation directly used for continuous action generation, improving policy learning efficiency while effectively leveraging human knowledge. Across RoboCasa-GR1, OOD settings, and real-world robot experiments, Nexus-VLA achieves an 80.5% task success rate on RoboCasa-GR1 and demonstrates strong advantages in human-data utilization, learning efficiency, and cross-scenario generalization.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.42021 — 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 2026-11-03T08:06:53.848Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).