用测试时计算提升机器人模仿学习的泛化能力
SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM
- 将模仿学习转化为可扩展的迭代优化问题
- 测试时计算越多，成功率越高，复杂任务达95%
- 适合需要高鲁棒性的真实场景机器人应用
上下文模仿学习让机器人能从示范中习得技能，但在环境变化下仍易失效。我们提出SAIL框架，将机器人模仿学习重构为可随测试时计算规模扩展的迭代优化问题。SAIL采用蒙特卡洛树搜索，每个节点代表一条完整轨迹，边表示轨迹改进。其核心由三部分构成：自动归档成功轨迹以支持情境相关检索；基于视觉语言模型的轨迹评分机制；以及对齐轨迹的逐步反馈评分，用于迭代优化。在六种不同模拟操作任务和真实世界验证中，增加测试时计算持续提升成功率，复杂任务最高达95%。结果表明，轨迹级测试时扩展是实现更通用机器人的稳健路径。
原文摘要 · Abstract (English)
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.
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