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DEXTERA: From a Single Image to Deployable Dexterous Manipulation via Real-to-Sim-to-Real — arXiv:2609.21045

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21045
License (as archived): CC0-1.0
Archived: 2026-10-10T02:29:57.019Z
SHA-256 of the archived text: 774a936e16a37f4d118d7575d50b2aca0102818b8206fdc92db1e7854926943c
Stable URL of this page: https://metraipolis.com/documento/dc_2f3dfec6bb9b

How to cite

DEXTERA: From a Single Image to Deployable Dexterous Manipulation via Real-to-Sim-to-Real — arXiv:2609.21045 — arXiv (en), https://arxiv.org/abs/2609.21045 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2f3dfec6bb9b (Metraipolis archive dc_2f3dfec6bb9b, sha256 774a936e16a37f4d118d7575d50b2aca0102818b8206fdc92db1e7854926943c)

BibTeX

@misc{dc2f3dfec6bb9b,
  title = {DEXTERA: From a Single Image to Deployable Dexterous Manipulation via Real-to-Sim-to-Real — arXiv:2609.21045},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21045},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2f3dfec6bb9b; sha256 774a936e16a37f4d118d7575d50b2aca0102818b8206fdc92db1e7854926943c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21045. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Collecting real-world robot data for dexterous manipulation is costly and time-consuming. While high-fidelity physics simulators enable scalable data synthesis and policy learning, constructing deployment-ready digital twins manually remains labor-intensive, and residual visual, geometric, and dynamics gaps hinder reliable sim-to-real transfer. We present DEXTERA, an automated real-to-sim-to-real framework that transforms a single RGB image into deployable policies for dexterous manipulation across four unified stages: (1) single-image scene factorization into a static Gaussian background and interactive rigid or articulated assets with VLM-inferred physical parameters; (2) metric scene global alignment, object canonicalization, and morphology-balanced robot calibration; (3) scalable simulator task primitive construction, VR teleoperation, and object-centric trajectory synthesis; and (4) a shared multimodal policy interface supporting both imitation learning and reinforcement learning. We evaluate DEXTERA across 13 task-embodiment pairs, 2 dexterous robot platforms, and 6 policy architectures. Experimental results demonstrate that DEXTERA achieves superior visual fidelity and 3D geometric reconstruction compared to generative baselines, while cross-domain trajectory replays validate strong physical interaction consistency. Furthermore, simulation-only trained policies enable viable zero-shot real-robot deployment, while simulation-real co-training substantially improves mean physical policy success from 29.2% to 61.9% across diverse policy architectures. Project website: this https URL

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-21 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.21045 — 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 2026-10-10T02:09:55.289Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).