We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms. Each robot design is represented by a structured grammar, and we use LLMs to efficiently explore this design space. Traditionally, such exploration is time-consuming and computationally intensive. Using a best-shot prompting strategy combined with reinforcement learning (RL)-based control evaluation, RoboMorph iteratively refines robot designs within an evolutionary feedback loop. Across four terrain types, RoboMorph discovers diverse, terrain-specialized morphologies, including wheeled quadrupeds and hexapods, that match or outperform designs produced by Robogrammar's graph-search method. These results demonstrate that LLMs, when coupled with evolutionary selection, can serve as effective generative operators for automated robot design. Our project page and code are available at https://robomorph.github.io.

翻译：我们提出RoboMorph，一种利用大型语言模型（LLMs）与进化算法自动生成并优化模块化机器人设计的自动化方法。每个机器人设计由结构化语法表示，我们借助LLMs高效探索该设计空间。传统上，此类探索耗时且计算强度大。通过采用最佳示例提示策略并结合基于强化学习（RL）的控制评估，RoboMorph在进化反馈循环中迭代优化机器人设计。在四种地形类型上，RoboMorph发现了多样化的、适应特定地形的形态（包括轮式四足与六足机器人），其性能匹配或超越了Robogrammar图搜索方法生成的设计。这些结果表明，当与进化选择相结合时，LLMs可作为机器人自动化设计的有效生成算子。我们的项目页面与代码见https://robomorph.github.io。