发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出用LLM构建显式工程模型，在优化前检查和修正建模假设，并在四旋翼和循线机器人组件选择中验证，证明该方法能提供可行性保证并整合工程知识与正式优化。
AI 中文摘要
大型语言模型（LLMs）能够为机器人设计贡献有用的工程知识，但直接生成的设计可能依赖于隐含假设，并且无法保证可行性或最优性。这些假设至关重要，因为不同的合理建模选择会实质性地改变哪些设计被预测为可行或最优。因此，我们提出一个框架，利用LLMs构建包含物理关系、兼容性约束和目标函数的显式工程模型，使得这些建模选择在正式优化之前可以被检查和修正。该模型可以在正式多目标优化之前，通过额外的工程、制造商或系统特定信息进行更新，从而针对最终确定的模型和指定的设计空间提供可行性和帕累托最优性保证。我们在四旋翼和循线机器人组件选择问题上评估了该框架。在30次直接LLM设计试验中，没有一次能在相应的最终模型下被验证为可行。与独立开发的专家模型以及模型细化的连续阶段的比较进一步表明，建模假设的变化会显著改变预测的可行和帕累托最优设计集合。这些结果共同表明，使用LLMs构建显式工程模型，使得底层设计选择在假设决定优化设计之前可供检查和修正。因此，显式建模提供了一个接口，用于结合LLM生成的工程知识、系统特定信息和正式的设计优化。
英文摘要
Large Language Models (LLMs) can contribute useful engineering knowledge to robot design, but directly generated designs may rely on implicit assumptions and provide no guarantees of feasibility or optimality. These assumptions are critical because different reasonable modeling choices can materially change which designs are predicted to be feasible or optimal. We therefore present a framework that uses LLMs to construct explicit engineering models containing physical relationships, compatibility constraints, and objectives, allowing these modeling choices to be inspected and revised before formal optimization. The model can then be updated with additional engineering, manufacturer, or system-specific information before formal multi-objective optimization provides feasibility and Pareto-optimality guarantees with respect to the finalized model and specified design space. We evaluate the framework on quadcopter and line-following robot component-selection problems. Across 30 direct LLM design trials, none could be verified as feasible under the corresponding finalized model. Comparisons with an independently developed expert model and successive stages of model refinement further showed that changes in modeling assumptions substantially altered the predicted feasible and Pareto-optimal design sets. Together, these results show that using LLMs to construct explicit engineering models makes the underlying design choices available for inspection and revision before those assumptions determine the optimized designs. Explicit modeling therefore provides an interface for combining LLM-generated engineering knowledge, system-specific information, and formal design optimization.
Comments9 pages, 4 figures
