RoboSnap: One-Shot Real-to-Sim Scene Generation for Generalizable Robot Learning and Evaluation
RoboSnap is a real-to-sim framework that turns a single RGB image into a simulation-ready scene using a layered design: collision-aware foreground assets for stable robot interaction and 3D Gaussian splatting for faithful background appearance. Experiments on DROID scenes and real-robot tasks show reliable trajectory replay, task-specific synthetic data generation, and meaningful sim-real correlation. The work also introduces DROID-Sim, a companion dataset of 564 real-world scenes.
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[Submitted on 7 Jul 2026]
Title:RoboSnap: One-Shot Real-to-Sim Scene Generation for Generalizable Robot Learning and Evaluation
View a PDF of the paper titled RoboSnap: One-Shot Real-to-Sim Scene Generation for Generalizable Robot Learning and Evaluation, by Shujie Zhang and 8 other authors
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Abstract:Recovering real-world scenes as interactive simulation environments can enable generalizable robot learning and reproducible policy evaluation. However, constructing scenes that are both physically stable and visually faithful remains slow and expensive. In this work, we present RoboSnap, a real-to-sim framework that turns a single RGB image into a simulation-ready scene. The key idea is a layered design that separates the physics-critical interaction area from the surrounding visual context: collision-aware foreground assets are refined for stable robot interaction, while a 3D Gaussian splatting visual layer preserves faithful background appearance under novel views. Experiments on DROID scenes and real-robot tasks show that RoboSnap achieves reliable trajectory replay in the recovered scenes, supports task-specific synthetic data generation for policy training, and yields meaningful sim-real correlation for policy evaluation. To further support real-to-sim research, we introduce DROID-Sim, a real-to-sim companion dataset constructed from 564 real-world scenes in DROID. Extensive experiments suggest that the value of real-to-sim methods lies not only in high-fidelity visual reconstruction, but in turning real environments into reusable infrastructure for robot learning and evaluation.
Comments: 24 pages, 16 figures, Project page: this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.06699 [cs.RO]
(or arXiv:2607.06699v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.06699
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shujie Zhang [view email] [v1] Tue, 7 Jul 2026 18:19:10 UTC (10,853 KB)
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