Stanford's Wetzstein endorses finding: web video pretraining scaling boosts real-world robot manipulation
GordonWetzstein · x · 2026-09-11
Gordon Wetzstein endorsed Tongzhou Mu's new research thread, calling it "a big step towards bringing robots outside the lab" into real-world useful tasks.
Key findings:
- Scaling pre-training on general web video substantially improves a complex real-deployment manipulation task
- The team scaled both model size and pre-training compute, testing on one industrial task
- The better a pre-trained model predicts web video, the better its post-trained policy performs
The result suggests web video prediction quality is a usable predictor of downstream robot policy capability.
More from Embodied
- Robotics startup Skild AI hits $100M ARR just 10 months after first deployment — deepakpathak · 2026-09-11
- McKinsey report calls humanoid robots a media distraction, sees just 2% of Physical AI market by 2045 — kscottz · 2026-09-11
- Simulated zebrafish and vision-equipped robot fish reveal how the body shapes brain circuits — DoctorJosh · 2026-09-11
- SpotHero + Tesla FSD gets you ~85% of a Waymo, says driver — cantrell · 2026-09-11
- Skild AI uses NVIDIA Physical AI to teach robots new tasks from a single video — nordicinst · 2026-09-11
- Play2Perfect (CoRL 2026): Play Pretraining Yields Precise Zero-Shot Robot Assembly — leto__jean · 2026-09-11