外观
论文arXiv 2610.1132220 天前
USDCraft：LLM驱动程序化建模生成仿真就绪铰接资产
本文由 AI 根据公开内容整理，可能有误，请以原文为准：USDCraft: Geometrically Grounded Programmatic Modeling of Articulated 3D Assets for Simulation（arXiv 2610.11322）
核心内容
针对真实物体网格残缺导致仿真策略迁移失败的问题，研究团队提出USDCraft框架，将铰接资产重建定义为基于部分几何证据的程序化建模任务。该方法无需任务特定训练，利用预训练大语言模型编写并修订可执行代码，自动生成仿真就绪的铰接资产。实验表明，该框架在两个主流基准测试中取得领先的铰接结构恢复性能，并成功验证了从真实场景到仿真再回到物理机器人的操作迁移能力。
技术核心在于源几何分析与迭代几何重校验机制：前者将源网格转换为区分已知表面与未知空间的度量文本描述；后者将候选程序重编码为相同表示，通过差异定位代码编辑点，同时保留未观测区域供补全。结合视觉反馈与物理属性编写指导，系统自动输出包含显式物理参数的USD格式资产，可直接加载至Isaac Sim仿真器而无需人工调整，解决了传统Mesh方法在分布外物体上的部署瓶颈。
关键看点
- 程序化建模：预训练LLM直接生成可执行代码重建铰接资产，无需任务特定微调即可处理分布外物体
- 几何感知机制：源几何分析将网格转为度量文本描述，精准区分已观测表面与未知空间以支持残缺修复
- 仿真无缝衔接：自动生成含显式物理属性的USD资产，零人工调整直接加载Isaac Sim进行策略训练
- Sim-to-Real验证：在两项基准测试中铰接恢复性能领先，并证实仿真训练策略可有效迁移至真实机器人操作
工程与产业观察
该工作确立了“LLM+程序化建模”作为具身智能仿真资产生产的新范式，有效破解了真实物体网格残缺导致的Sim-to-Real鸿沟。其生成的标准化USD资产与Isaac Sim原生兼容，显著降低了高保真铰接物体数据集的构建门槛，为机器人操作策略的泛化训练提供了可扩展的工程基础设施。
英文摘要 (Abstract)
Geometrically faithful and functional articulated 3D assets are essential for real-to-sim robot manipulation, where policies trained in simulation must transfer to physical objects. Recent mesh-based methods learn to infer articulation from annotated 3D assets, but deployment remains challenging when real-world objects fall outside the training distribution or their meshes are incomplete or corrupted. To address these limitations, we formulate articulated asset reconstruction as programmatic modeling grounded in partial geometric evidence and introduce USDCraft, a framework in which a pretrained LLM writes and revises executable programs for simulation-ready articulated assets without task-specific training. We propose source geometry analysis, which converts the source mesh into a metric textual description that distinguishes observed surface from unknown space, and iterative geometric rechecking, which re-encodes each candidate in the same representation so that discrepancies point to program edits while unobserved regions remain open to completion. Visual feedback and physical authoring guidance complete the modeling process, which produces articulated USD assets with explicit physical properties that load into Isaac Sim without manual adjustment. Experiments demonstrate leading articulation recovery on two benchmarks and validate USDCraft's effectiveness for real-to-sim-to-real robot manipulation.
作者：Chuanrui Zhang, Zaijia Yang, Duomin Wang, Lu Shi, Daquan Zhou, Ruihua Zhang, Ziwei Wang
来源
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