用机器人自动采集真实物体的物理属性，生成可直接用于仿真的数字资产。
Scalable Real2Sim: Physics-Aware Asset Generation Via Robotic Pick-and-Place Setups
- 通过机器人抓取+摄像头+扭矩传感器自动提取物体几何与物理参数。
- 在多种物体上验证，生成的仿真资产能准确还原真实动态行为。
- 无需人工干预，可直接接入现有机械臂产线，适合大规模数据构建。
从真实世界感知中模拟物体动力学在数字孪生和机器人操作中具有巨大潜力，但通常需要大量人工测量和专业知识。我们提出一个全自动的Real2Sim流程，通过机器人交互生成可用于仿真的真实物体资产。仅需机器人关节扭矩传感器和外部摄像头，该流程即可识别物体的视觉几何、碰撞几何及惯性参数等物理属性。方法创新性地在光度重建技术（如NeRF、Gaussian Splatting）中引入透明掩码训练，明确区分前景遮挡与背景剔除，从而获得高质量、以物体为中心的网格。我们在多样物体上进行了充分实验验证，证明了该流程的有效性。通过消除对人工干预或环境改造的需求，本流程可直接集成到现有抓取-放置系统中，实现可扩展、高效的仿真数据生成。项目页面（含代码与数据）：https://scalable-real2sim.github.io/。
原文摘要 · Abstract (English)
Simulating object dynamics from real-world perception shows great promise for digital twins and robotic manipulation but often demands labor-intensive measurements and expertise. We present a fully automated Real2Sim pipeline that generates simulation-ready assets for real-world objects through robotic interaction. Using only a robot's joint torque sensors and an external camera, the pipeline identifies visual geometry, collision geometry, and physical properties such as inertial parameters. Our approach introduces a general method for extracting high-quality, object-centric meshes from photometric reconstruction techniques (e.g., NeRF, Gaussian Splatting) by employing alpha-transparent training while explicitly distinguishing foreground occlusions from background subtraction. We validate the full pipeline through extensive experiments, demonstrating its effectiveness across diverse objects. By eliminating the need for manual intervention or environment modifications, our pipeline can be integrated directly into existing pick-and-place setups, enabling scalable and efficient dataset creation. Project page (with code and data): https://scalable-real2sim.github.io/.
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