SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM
In-context imitation learning allows robots to acquire skills from demonstrations, yet one-shot trajectory generation remains fragile under environmental variation. We propose SAIL, a framework that reframes robot imitation as an iterative refinement problem capable of scaling with test-time compute. SAIL utilizes Monte Carlo Tree Search, where each node is a complete trajectory and edges correspond to trajectory refinements. The process is guided by three core components: an automated archive of successful trajectories for contextually relevant retrieval, a vision language model-based scoring mechanism for trajectory evaluation, and a step-level feedback that provides trajectory-aligned ...
| Authors | Makoto Sato, Yusuke Iwasawa, Yujin Tang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 March 2026 |
| Last updated at source | 19 September 2026 |
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