When Search Becomes Memory: Accelerating Robot Design Discovery with Self-Evolving Skills

The paper introduces Auto‑Robotist, a self‑evolving large language model (LLM) agent that transforms evolutionary robot design search traces into an explicit natural‑language skill library. Each skill records a structural archetype, evidence‑grounded rules, and supporting designs, enabling the agent to retrieve and condition LLM edits during search while still using a genetic algorithm for exploration. Experiments on seven EvoGym tasks show that Auto‑Robotist outperforms standard genetic algorithms, especially when transferring learned skills to larger design spaces.

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• llms
• agents
• robotics