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Agentic RSR: Real-to-Sim-to-Real through Scene Reconstruction and Execution-Grounded Robot Policies — arXiv:2610.10479

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.10479
License (as archived): CC0-1.0
Archived: 2026-10-08T12:48:45.293Z
SHA-256 of the archived text: 31f17b7858a9329c9d9946b133a0ed1f6c21d1efb1ade432bacc5e267a893d79
Stable URL of this page: https://metraipolis.com/documento/dc_8bfd71abb3ea

How to cite

Agentic RSR: Real-to-Sim-to-Real through Scene Reconstruction and Execution-Grounded Robot Policies — arXiv:2610.10479 — arXiv (en), https://arxiv.org/abs/2610.10479 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8bfd71abb3ea (Metraipolis archive dc_8bfd71abb3ea, sha256 31f17b7858a9329c9d9946b133a0ed1f6c21d1efb1ade432bacc5e267a893d79)

BibTeX

@misc{dc8bfd71abb3ea,
  title = {Agentic RSR: Real-to-Sim-to-Real through Scene Reconstruction and Execution-Grounded Robot Policies — arXiv:2610.10479},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.10479},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8bfd71abb3ea; sha256 31f17b7858a9329c9d9946b133a0ed1f6c21d1efb1ade432bacc5e267a893d79},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10479. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A simulation of a real robot workspace must preserve task-relevant interactions, while policies developed in it must operate on observations available to the real robot. Yet scene reconstruction and policy development are often treated separately. We present Agentic Real-to-Sim-to-Real (Agentic RSR), a framework that links scene reconstruction, policy development, and real-robot execution through the same manipulation task. Given a workspace video, a task description, and a known robot model, an agent recovers metric scale, iteratively refines the scene using visual feedback, and checks task-relevant interactions in MuJoCo. A coding agent then develops an executable policy, progressing from privileged object poses to visual observations and randomized simulation. The policy can interleave multiple observations and actions within one invocation, while the agent uses execution feedback to continue, retry, or revise its approach. A shared task-level interface carries the policy and accumulated experience to the real robot, where fresh observations and safety checks guide execution. Across 18 reconstructed scenes involving two robots, the mean four-view Depth MAE against reference depth estimates is 0.1057 m, the mean Lab $\Delta E_{76}$ is 11.04, and the mean grayscale SSIM is 0.6990. In real-robot experiments, the aggregate task success rate reaches 80% of the simulation task success rate, indicating substantial retention of simulated performance on hardware. Code and reconstructed scene data will be made publicly available.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.10479 — 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 2026-10-08T12:18:15.542Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).