Author

Makoto Sato

2 papers indexed here

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#artificial intelligence Mar 2026

SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM

SAIL is a framework that reframes robot imitation as an iterative refinement problem capable of scaling with test-time compute, and utilizes Monte Carlo Tree Search, where each node is a complete trajectory and edges correspond to trajectory refinements.

Makoto Sato, Yusuke Iwasawa, Yu-Jin Tang et al. · arXiv.org · 2 citations

Preprint Aug 2026

DREAM: Deployment-Time Demonstration Generation via Real-to-Sim for Scalable Policy Adaptation

DREAM is presented, a framework that generates fine-tuning data for a pretrained VLA from a captured workspace and a language instruction, without requiring a task-specific human demonstration, and whether it can serve as a scalable data-collection system for the deployment workspace.

Makoto Sato, T. Matsushima, Yutaka Matsuo et al. · 2 citations