ArtiCAD: 通过多智能体代码生成实现可动CAD装配设计
ArtiCAD: Articulated CAD Assembly Design via Multi-Agent Code Generation
AI总结：
ArtiCAD通过多智能体系统从文本或图像生成可编辑的可动CAD装配，利用连接器定义连接点和关节参数，提升空间推理能力，通过验证步骤和回滚机制确保输出质量，验证了其在概念设计、物理原型和AI训练资产生成中的应用。
摘要：
参数化计算机辅助设计（CAD）对于可动装配至关重要，但从高层描述生成多部件可动模型仍属未探索领域。为此，我们提出ArtiCAD，首个无需训练的多智能体系统，可直接从文本或图像生成可编辑的可动CAD装配。系统将复杂任务分配给四个专门智能体：设计、生成、装配和审查。关键见解是预测装配关系在初始设计阶段而非装配阶段。通过连接器显式定义连接点和关节参数，ArtiCAD在几何生成前确定这些关系，有效绕过当前LLMs和VLMs的有限空间推理能力。为进一步确保高质量输出，我们在生成和装配阶段引入验证步骤，并辅以跨阶段回滚机制，准确隔离和纠正设计及代码层面的错误。此外，自演化经验库积累设计知识，持续提升未来任务性能。在三个数据集（ArtiCAD-Bench、CADPrompt和ACD）上的广泛评估验证了方法的有效性。我们进一步展示了ArtiCAD在需求驱动的概念设计、物理原型生成以及生成具身AI训练资产中的应用通过URDF导出。
英文摘要：
Parametric Computer-Aided Design (CAD) of articulated assemblies is essential for product development, yet generating these multi-part, movable models from high-level descriptions remains unexplored. To address this, we propose ArtiCAD, the first training-free multi-agent system capable of generating editable, articulated CAD assemblies directly from text or images. Our system divides this complex task among four specialized agents: Design, Generation, Assembly, and Review. One of our key insights is to predict assembly relationships during the initial design stage rather than the assembly stage. By utilizing a Connector that explicitly defines attachment points and joint parameters, ArtiCAD determines these relationships before geometry generation, effectively bypassing the limited spatial reasoning capabilities of current LLMs and VLMs. To further ensure high-quality outputs, we introduce validation steps in the generation and assembly stages, accompanied by a cross-stage rollback mechanism that accurately isolates and corrects design- and code-level errors. Additionally, a self-evolving experience store accumulates design knowledge to continuously improve performance on future tasks. Extensive evaluations on three datasets (ArtiCAD-Bench, CADPrompt, and ACD) validate the effectiveness of our approach. We further demonstrate the applicability of ArtiCAD in requirement-driven conceptual design, physical prototyping, and the generation of embodied AI training assets through URDF export.
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。
