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SAIL: Scaling In-Context Imitation Learning
Method for reliable robot trajectory generation via test-time scaling
Sakana AI and the University of Tokyo propose SAIL, a method for VLM-based robot trajectory generation that uses test-time scaling with Monte Carlo tree search to refine trajectories in simulation.
In six simulated manipulation tasks, increasing the search budget from one to 45 candidates raised the success rate from 25% to 73%.
The method was also evaluated on a physical robot.
From the source
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%.
sakana.ai
