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

• URL: http://arxiv.org/abs/2505.14938v1
• Date: Tue, 20 May 2025 21:55:01 GMT
• Title: Scan, Materialize, Simulate: A Generalizable Framework for Physically Grounded Robot Planning
• Authors: Amine Elhafsi, Daniel Morton, Marco Pavone,
• Abstract summary: Scan, Materialize, Simulate (SMS) is 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.<n>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.
• Score: 16.193477346643295
• License: http://creativecommons.org/licenses/by/4.0/
• 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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