SEAR: Self-Evolving Agentic Robotics
Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for the next task. We introduce SEAR (Self-Evolving Agentic Robotics), a framework and benchmark built around two questions. Is the agent really learning, or just trying more often? And does what it learns still help on new tasks, in a new simulator, and on a real robot? The framework runs an agent in three modes: it writes code that becomes the policy (code-as-policy), acts live in the control loop (agent-as-policy), or works as a researcher that fine-tunes a vision-language-action model (auto-research). The benchmark adds controls that separate...
| Authors | Xinyi Wang, Bangzheng Li, Hanyu Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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