当搜索成为记忆：将机器人设计试验转化为可迁移技能
When Search Becomes Memory: Accelerating Robot Design Discovery with Self-Evolving Skills
AI总结：
提出Auto-Robotist，一种自进化LLM代理，通过将形态搜索轨迹提炼为自然语言技能库，实现可迁移的机器人设计知识，在EvoGym任务中提升冷启动搜索并跨设计空间迁移技能。
摘要：
大型语言模型（LLMs）越来越多地被用作进化机器人设计的提案生成器，但大多数循环仍然是无记忆的：模拟结果塑造下一代种群，但并未作为可复用的设计知识保留。我们提出Auto-Robotist，一种自进化的LLM代理，它将形态搜索轨迹提炼为显式的自然语言技能库。每个技能存储结构原型、基于证据的正负规则以及支持它们的评估设计，使设计记忆可检查而非隐含在种群中。在搜索过程中，代理检索技能以调节LLM对精英主体的编辑，同时保留遗传算法（GA）突变路径以进行探索；评估后，通过添加、诊断和合并更新库。在涵盖运动、穿越和物体交互的七个EvoGym任务中，Auto-Robotist改善了冷启动5x5搜索，并将学到的技能迁移到10x10设计空间，其中参考条件迁移在每个任务上都优于GA。这些结果表明，LLM代理可以将昂贵的物理评估转化为可复用、可审计的设计原则。我们的代码将在接收后发布。
英文摘要：
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 is publicly available at https://github.com/wangyf9/Auto-Robotist .
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。
