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arXiv · 2505.14938

Scan, Materialize, Simulate: A Generalizable Framework for Physically Grounded Robot Planning

Amine Elhafsi·Daniel Morton·Marco Pavone
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Abstract

Autonomous robots must reason about the physical consequences of their actions to operate effectively in unstructured, real-world environments. We present Scan, Materialize, Simulate (SMS), a unified framework that combines 3D Gaussian Splatting for accurate scene reconstruction, visual foundation models for semantic segmentation, vision-language models for material property inference, and physics simulation for reliable prediction of action outcomes. By integrating these components, SMS enables generalizable physical reasoning and object-centric planning without the need to re-learn foundational physical dynamics. We empirically validate SMS in a billiards-inspired manipulation task and a challenging quadrotor landing scenario, demonstrating robust performance on both simulated domain transfer and real-world experiments. Our results highlight the potential of bridging differentiable rendering for scene reconstruction, foundation models for semantic understanding, and physics-based simulation to achieve physically grounded robot planning across diverse settings.

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BibTeXRIS

Amine Elhafsi, Daniel Morton, Marco Pavone. 2025-05-20. Scan, Materialize, Simulate: A Generalizable Framework for Physically Grounded Robot Planning. https://arxiv.org/abs/2505.14938

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