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arXiv · 2410.15536

GRS: Generating Robotic Simulation Tasks from Real-World Images

Alex Zook·Fan-Yun Sun·Josef Spjut·Valts Blukis·Stan Birchfield·Jonathan Tremblay
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Abstract

We introduce GRS (Generating Robotic Simulation tasks), a system addressing real-to-sim for robotic simulations. GRS creates digital twin simulations from single RGB-D observations with solvable tasks for virtual agent training. Using vision-language models (VLMs), our pipeline operates in three stages: 1) scene comprehension with SAM2 for segmentation and object description, 2) matching objects with simulation-ready assets, and 3) generating appropriate tasks. We ensure simulation-task alignment through generated test suites and introduce a router that iteratively refines both simulation and test code. Experiments demonstrate our system's effectiveness in object correspondence and task environment generation through our novel router mechanism.

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Alex Zook, Fan-Yun Sun, Josef Spjut, Valts Blukis, Stan Birchfield, Jonathan Tremblay. 2024-10-20. GRS: Generating Robotic Simulation Tasks from Real-World Images. https://arxiv.org/abs/2410.15536

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