Tomohiro Motoda

Stochastic action tokenization unlocks significant performance gains in robot policy learning, especially under data scarcity, by leveraging representational redundancy.

A modular benchmark that adapts to diverse insertion scenarios reveals critical insights into robotic generalization capabilities in high-precision tasks.

The embodiment gap reveals that even advanced robot foundation models require substantial adaptation to function on specific robotic platforms, challenging the assumption that scaling alone suffices for generalization.

Immediate feedback-driven corrections can enhance robotic manipulation performance without the overhead of retraining entire policies.

Correctable visual attention can drastically enhance robot manipulation performance, especially in unpredictable environments.