Yugo Ito

High school student working on robot learning, embodied AI, and simulation.

I build small-scale research projects around manipulation, synthetic data, and world representations.

Current research

GYOZA

Measure RoboCasa reset/step and held-out success before proceeding to RL.

Code-as-World

Recheck the submitted v3 result before claiming model capability.

Synthetic Data in Robot Learning

Test the perception-bottleneck reproduction before fixing relative baselines.

Research record

GYOZA

A robot-learning project for tabletop food manipulation, orchestration, and verification.

Role
Design, experiment, and verification of VLM×ACT autonomous robot-arm systems, centered on sim-to-real and human-video transfer.
Evidence
  • Ran manipulation experiments on the physical SO-101 arm (median RTT 658ms)
  • Passed the MisoBowlToTray smoke test in the RoboCasa environment
  • Recorded a no-go decision on Web-video pseudo-actions in GYOZA-Scaling
Next
Measure RoboCasa reset/step and held-out success before proceeding to RL.

Code-as-World

A world-model direction that treats environments as executable scene descriptions.

Role
Designed the conditional world-model gate evaluation and a safe deterministic evaluator.
Evidence
  • Implemented the Stage 0A white-ball world-code benchmark
  • Verified cwd via HF Jobs / Pi integration
  • Confirmed Linux EGL/OSMesa as the rendering acceptance path
Next
Recheck the submitted v3 result before claiming model capability.

Synthetic Data in Robot Learning

Experiments on when synthetic data helps behavior—and when perception becomes the bottleneck.

Role
Designed the synthetic-data pipeline and experiments separating behavior from perception.
Evidence
  • Recorded the observation that perception, not behavior, becomes the bottleneck in synthetic-data learning
  • Experiment logs to be published in Notes
Next
Test the perception-bottleneck reproduction before fixing relative baselines.