aiqbench analysis
SAIL introduces a novel framework that leverages test-time compute and vision language models to significantly improve the robustness of robotic imitation learning under environmental variations. By reframing trajectory generation as an iterative refinement problem guided by Monte Carlo Tree Search, the method achieves high success rates across diverse manipulation tasks without requiring retraining. This approach marks a meaningful step toward creating more generalizable autonomous agents capable of adapting dynamically at inference time.
SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM
arXiv:2603.08269v2 Announce Type: replace-cross Abstract: 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 scores for iterative refinement. Experiments across six diverse manipulation tasks in simulation and real-world validation clearly demonstrate that increasing test-time compute consistently improves success rates, achieving up to 95% on complex tasks. Our results suggest that trajectory-level test-time scaling is a robust path toward more generalizable robotic agents.
Abstract: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 scores for iterative refinement. Experiments across six diverse manipulation tasks in simulation and real-world validation clearly demonstrate that increasing test-time compute consistently improves success rates, achieving up to 95% on complex tasks. Our results suggest that trajectory-level test-time scaling is a robust path toward more generalizable robotic agents.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2603.08269 [cs.RO]
(or arXiv:2603.08269v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.08269 Focus to learn more arXiv-issued DOI via DataCite
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