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JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment — arXiv:2608.05674

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05674
License (as archived): CC0-1.0
Archived: 2026-10-31T10:38:14.410Z
SHA-256 of the archived text: dd7d353eea1860e2e489311e172824cb57cbd485eceedafd4223c9c8ba2c7d4e
Stable URL of this page: https://metraipolis.com/documento/dc_be2db22cb5ea

How to cite

JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment — arXiv:2608.05674 — arXiv (en), https://arxiv.org/abs/2608.05674 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_be2db22cb5ea (Metraipolis archive dc_be2db22cb5ea, sha256 dd7d353eea1860e2e489311e172824cb57cbd485eceedafd4223c9c8ba2c7d4e)

BibTeX

@misc{dcbe2db22cb5ea,
  title = {JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment — arXiv:2608.05674},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05674},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_be2db22cb5ea; sha256 dd7d353eea1860e2e489311e172824cb57cbd485eceedafd4223c9c8ba2c7d4e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05674. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Robot data is scarce, so generalist policies need to learn from heterogeneous sources, including human egocentric video, simulation, and real robots, which differ in supervision and embodiment, with action labels missing or mutually incompatible. Human egocentric data scale best but sit farthest from robot data, and naive pooling causes negative transfer rather than knowledge sharing. We propose JoyAI-RA 0.5, a generalist Vision-Language-World-Action (VLWA) framework that couples physical world-dynamics priors with visual semantics and scales manipulation learning across such data via dual action alignment. Implicit action alignment infers latent actions from visual transitions, enabling action-free human, simulation, and robot data to guide a latent-action-conditioned world model in learning physical dynamics. Explicit alignment grounds reliable human and robot trajectories in a unified physical action space through a canonical action representation and camera-frame chunk-relative end-effector actions. An inner-outer-loop reinforcement stage then pairs efficient task adaptation with foundation-policy improvement. On a real-world AgiBot benchmark, JoyAI-RA performs strongly on both seen tasks and unseen variations. The task score improves consistently as the volume of human egocentric pretraining data increases and shows no sign of plateauing at our largest scale. This suggests that abundant but weakly labeled human experience can be converted into a transferable training signal, making human video not merely a weak auxiliary source but a primary axis along which manipulation capability can be scaled. Project page can be found at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-07 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2608.05674 — 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 2026-10-31T10:24:02.097Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-08-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).