RoboSnap: One-Shot Real-to-Sim Scene Generation for Generalizable Robot Learning and Evaluation
Overview
This paper introduces RoboSnap, a real-to-sim framework that generates simulation-ready scenes from a single RGB image by separating physics-critical foreground assets for stable interaction from a 3D Gaussian splatting visual layer for faithful background appearance. The authors also release DROID-Sim, a companion dataset comprising 564 real-world scenes from the DROID benchmark. Experiments demonstrate that RoboSnap enables reliable trajectory replay, supports task-specific synthetic data generation for policy training, and provides meaningful sim-real correlation for evaluation, positioning real-to-sim methods as reusable infrastructure for robot learning.
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- arXiv Robotics — research abstractsSignalRoboSnap: One-Shot Real-to-Sim Scene Generation for Generalizable Robot Learning and Evaluation
This paper introduces RoboSnap, a real-to-sim framework that generates simulation-ready scenes from a single RGB image by separating physics-critical foreground assets for stable interaction from a 3D Gaussian splatting visual layer for faithful background appearance. The authors also release DROID-Sim, a companion dataset comprising 564 real-world scenes from the DROID benchmark. Experiments demonstrate that RoboSnap enables reliable trajectory replay, supports task-specific synthetic data generation for policy training, and provides meaningful sim-real correlation for evaluation, positioning real-to-sim methods as reusable infrastructure for robot learning.
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