Scan, Materialize, Simulate：一种面向物理落地机器人规划的可泛化框架
Scan, Materialize, Simulate: A Generalizable Framework for Physically Grounded Robot Planning
AI总结：
针对自主机器人在非结构化环境的物理推理需求，提出SMS框架，融合3D高斯重建、多模态基础模型与物理仿真，在操作与着陆任务中验证了其跨域鲁棒的物理落地规划能力。
摘要：
自主机器人若要在非结构化的真实环境中有效运行，必须能够推理自身动作产生的物理后果。我们提出了Scan, Materialize, Simulate（简称SMS，即扫描、实体化、模拟）统一框架，该框架结合了用于精准场景重建的3D Gaussian Splatting（3D高斯泼溅）、用于语义分割的视觉基础模型、用于材质属性推断的视觉-语言模型，以及用于可靠预测动作结果的物理仿真器。通过整合这些组件，SMS无需重新学习基础物理动力学，即可实现可泛化的物理推理和以物体为中心的规划。我们在受台球启发的操作任务以及具有挑战性的四旋翼着陆场景中对SMS进行了实验验证，证明其在仿真域迁移和真实世界实验中均表现出鲁棒性能。我们的研究结果凸显了衔接可微渲染场景重建、基础模型语义理解与基于物理的仿真，在多样化场景中实现物理落地机器人规划的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
