We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms. In this framework, we represent each robot design as a grammar and leverage the capabilities of LLMs to navigate the extensive robot design space, which is traditionally time-consuming and computationally demanding. By integrating automatic prompt design and a reinforcement learning based control algorithm, RoboMorph iteratively improves robot designs through feedback loops. Our experimental results demonstrate that RoboMorph can successfully generate nontrivial robots that are optimized for a single terrain while showcasing improvements in morphology over successive evolutions. Our approach demonstrates the potential of using LLMs for data-driven and modular robot design, providing a promising methodology that can be extended to other domains with similar design frameworks.

RoboMorph是一种利用大型语言模型和进化算法生成和优化模块化机器人设计的自动化方法，通过自动提示设计和基于强化学习的控制算法，RoboMorph通过反馈循环迭代改进机器人设计，实验结果表明，RoboMorph能够成功生成优化于单一地形的非平凡机器人，并展示了形态学在连续进化中的改进，该方法显示了使用大型语言模型来进行数据驱动和模块化机器人设计的潜力，为类似设计框架的其他领域提供了有希望的方法论。

RoboMorph: 利用大型语言模型演化机器人形态