Sakana AI 与东京大学提出 SAIL：用测试时扩展提升机器人上下文模仿学习
SAIL: Scaling In-Context Imitation Learning
Sakana AI 与东京大学合作提出 SAIL，通过测试时扩展让 VLM 更可靠地生成机器人轨迹：策略 VLM 基于少量成功演示生成轨迹，在模拟器中测试并由评估 VLM 审查视频找出卡点，再用 MCTS 探索并修正候选轨迹，仅将选定轨迹下发实体机器人。在六项模拟操作任务中，搜索候选数从 1 增至 45，平均成功率从 25% 提升至 73%。该工作将在 IROS2026 展示。
Introducing “Scaling In-Context Imitation Learning” (SAIL) to be presented at IROS2026. This work is a collaboration between Sakana AI and the University of Tokyo.
What does a robot need before it can tackle a new task?
Teaching a robot something new usually starts with collecting demonstrations and training a policy. But foundation models have already learned from vast amounts of images, text, and robotics-related data. We wanted to see how much of that knowledge we could draw out for robot control without changing the model itself.
Recent demonstrations suggest that GPT-6 Astra can operate physical robots alongside its general language and vision capabilities. Earlier work has also shown that LLMs/VLMs can generate entire sequences of robot movements from a few demonstrations.
However, a foundation model does not necessarily produce a reliable robot trajectory in a single generation. Performance depends on the context provided, and a small error in a movement target can cause the entire task to fail.
We propose SAIL, a method for more reliable VLM-based robot trajectory generation through test-time scaling.
SAIL uses a policy VLM as a robot trajectory generator, conditioned on a few successful demonstrations. It tests the generated trajectory in a simulator and uses an evaluation VLM to review the resulting video and identify where progress stalled. The policy VLM then uses this feedback to revise the trajectory, with Monte Carlo tree search (MCTS) exploring alternatives while refining promising candidates. Only the selected trajectory is sent to the physical robot.
Across six manipulation tasks in simulation, increasing the search budget from one candidate to 45 raised the average rate of finding a successful trajectory from 25% to 73%. We also evaluated SAIL on a physical robot. Our results suggest that robot trajectory generation can benefit from test-time scaling, with additional computation enabling the model to test and refine its proposed actions in simulation.
We think there is more to learn about what existing models can do with this kind of feedback, and how far those improvements carry over to physical robots.
来源：Sakana AI Blog · sakana.ai