VLCP: Vision Language Control Policy Closed-Loop Code Replanning for Robot Manipulation
Turning a frontier vision-language model into a robot policy usually means fine-tuning it to emit an action representation it never saw in pretraining, which throws away much of the reasoning that made the model worth reaching for. We go the other way and keep the VLM frozen. It writes the policy as a short Python control function, with no demonstrations and no fine-tuning. Writing that code once is open-loop, though. Existing closed-loop methods react at the wrong level: they retry a fixed policy or pick a different subtask, but never rewrite the code that failed. VLCP closes the loop where the failure actually lives, on the control code, within a single episode. Every $K$ steps the VLM ...
| Authors | Dhia Naouali, Minghan Wu, Claudia Wong et al. |
|---|---|
| Primary category | cs.RO |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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