SAIL proposes testing robot movement plans in simulation before physical runs
Sakana AI says its collaboration with the University of Tokyo generates robot trajectories from a few demonstrations, then tests and revises them using simulation feedback. Only the selected trajectory goes to the physical robot.
TLDR
Sakana AI introduced SAIL, a University of Tokyo collaboration slated for IROS 2026. The team says the method uses one vision-language model to generate robot movement plans and another to assess simulated runs, helping it refine plans without updating model weights. Across six manipulation tasks in simulation, raising the search budget from one candidate to 45 increased the average rate of finding a successful trajectory from 25% to 73%. The team also evaluated SAIL on a physical robot.
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