Sakana AI 提出机器人轨迹生成方法 SAIL，将亮相 IROS 2026
Sakana AI 和东大搞了个给机器人“先在模拟器里彩排再上场”的方法 SAIL，成功率能从 25% 拉到 73%，做机器人方向的可以看看。
Sakana AI 与东京大学合作提出 SAIL，将在 IROS 2026 上发表。该方法用策略 VLM 生成机器人轨迹，在模拟器中测试后由评估 VLM 看视频找出卡点，再用 MCTS 搜索备选方案迭代修正。在六个模拟操作任务上，搜索预算从 1 个候选增至 45 个，成功轨迹的发现率从 25% 提升到 73%。团队还在实体机器人上做了验证。
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.
Blog: https://t.co/b6xgwIMkUP
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.
Paper: https://t.co/robokip5Nc 🐟
