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Opt2VLA: Force-Aware Vision-Language-Action for Contact-Rich Humanoid Whole-Body Manipulation — arXiv:2609.23968

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23968
License (as archived): CC0-1.0
Archived: 2026-10-09T22:24:10.713Z
SHA-256 of the archived text: fcb142d89fc1b75ad26ef6b3909f1351aaf5c906d7af929b9073e491ac0699a9
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How to cite

Opt2VLA: Force-Aware Vision-Language-Action for Contact-Rich Humanoid Whole-Body Manipulation — arXiv:2609.23968 — arXiv (en), https://arxiv.org/abs/2609.23968 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_1d80bbdebf04 (Metraipolis archive dc_1d80bbdebf04, sha256 fcb142d89fc1b75ad26ef6b3909f1351aaf5c906d7af929b9073e491ac0699a9)

BibTeX

@misc{dc1d80bbdebf04,
  title = {Opt2VLA: Force-Aware Vision-Language-Action for Contact-Rich Humanoid Whole-Body Manipulation — arXiv:2609.23968},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23968},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_1d80bbdebf04; sha256 fcb142d89fc1b75ad26ef6b3909f1351aaf5c906d7af929b9073e491ac0699a9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23968. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Humanoid robots are expected to perform diverse human-level tasks in daily environments, many of which require precise regulation of interaction forces. While recent vision-language-action (VLA) models have shown promise for semantic planning and visuomotor control, existing humanoid systems primarily represent actions through geometric motion goals and rely on whole-body controllers focused on motion tracking, with limited explicit reasoning or control of interaction forces. This limitation is particularly relevant in contact-rich tasks, where geometrically similar motions may require different force regimes depending on the task context and where visual observations may become unreliable after contact. In this work, we present Opt2VLA, a force-aware VLA framework that introduces explicit force commands at the VLA-to-control interface for humanoid whole-body manipulation. A single multi-task VLA policy jointly predicts both geometric motion goals and continuous contact-force references, which are tracked by task-specific reinforcement learning (RL)-based whole-body controllers. To provide scalable and physically grounded supervision, we generate dynamically feasible and contact-consistent training data via whole-body trajectory optimization (TO) with explicit force references. We evaluate Opt2VLA on three contact-rich humanoid tasks and show that explicit force conditioning enables more accurate and consistent force regulation than motion-only control, while physically grounded torque supervision from TO further improves force tracking accuracy and stability. Closed-loop evaluations further demonstrate language-conditioned force modulation with Opt2VLA in simulation and on humanoid hardware.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-22 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.23968 — 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 2026-10-09T21:59:59.653Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).