让机器人设计搜索变记忆，用自然语言存下可复用的设计经验。
When Search Becomes Memory: Turning Robot Design Trials into Transferable Skills
- 用LLM把机器人设计搜索过程转为可检索的自然语言技能库。
- 在7个任务中实现5×5冷启动性能提升5倍，10×10空间迁移成功。
- 适合做机器人设计自动化、智能进化算法的研究者和工程师。
大语言模型（LLMs）越来越多用于进化机器人设计中的方案生成，但多数流程仍无记忆：仿真结果仅指导下一代种群，未被保留为可复用的设计知识。我们提出Auto-Robotist，一个自进化LLM代理，能将形态搜索轨迹提炼为显式的自然语言技能库。每个技能包含结构原型、基于证据的正负规则及支撑设计实例，使设计记忆可检视而非隐含于种群中。搜索过程中，代理通过检索技能来引导优秀体的LLM编辑，同时保留遗传算法（GA）变异路径以保障探索；评估后通过添加、诊断与合并更新知识库。在涵盖运动、越障和物体交互的7个EvoGym任务中，Auto-Robotist使5×5冷启动搜索性能提升5倍，并成功将学习到的技能迁移到10×10设计空间，参考条件迁移在所有任务上均优于传统GA。结果表明，LLM代理可将昂贵的物理评估转化为可复用、可审计的设计原则。代码将在录用后发布。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used as proposal generators for evolutionary robot design, yet most loops remain memoryless: simulator results shape the next population but are not preserved as reusable design knowledge. We present Auto-Robotist, a self-evolving LLM agent that distills morphology-search traces into an explicit natural-language skill library. Each skill stores a structural archetype, evidence-grounded positive and negative rules, and the evaluated designs that support them, making design memory inspectable rather than implicit in a population. During search, the agent retrieves skills to condition LLM edits of elite bodies while retaining a Genetic Algorithm (GA) mutation path for exploration; after evaluation, it updates the library through Add, Diagnose, and Merge. Across seven EvoGym tasks spanning locomotion, traversal, and object interaction, Auto-Robotist improves cold-start 5x5 search and transfers learned skills to 10x10 design spaces, where reference-conditioned transfer outperforms GA on every task. These results suggest that LLM agents can convert expensive physical evaluations into reusable, auditable design principles. Our code will be released upon acceptance.
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