Rhoda AI: scaling web-video pre-training keeps improving real-robot manipulation policies
stepjamUK · x · 2026-09-16
Rhoda AI rigorously tests the belief that scaling web-video pre-training improves downstream robot performance, using Direct Video-Action models (DVA) evaluated on a real industrial manipulation task across model sizes and compute budgets.
Key findings from 200+ hours of real-robot evaluation:
- Larger pre-trained video models consistently yield better robot policies, with no saturation at the largest size tested
- More pre-training compute helps at every amount of robot demonstration data, with the biggest gains when robot data is scarce
- Better held-out web-video prediction correlates with better real-robot policies across all scales
The results support scaling web-video pre-training as a core recipe for robot foundation models.
More from Embodied
- Autonomous's Harness Pro uses an ESP32-P4, not a mini PC, to monitor AI coding agents — dee_hw · 2026-11-02
- KAIST's QCP lets robot policies control execution quality in real time — kaist-ai · 2026-11-02
- ANYbotics begins customer deliveries of ANYmal X, first legged robot certified for explosive atmospheres — lukas_m_ziegler · 2026-11-02
- InSpatio opens China's first embodied simulation data center targeting 1M hours/month — 智东西 · 2026-11-02
- GR-BC mines better trajectories from imperfect demos, hitting ~91% success on precise manipulation — 机器之心 · 2026-11-02
- RAM prices tripled, so Tesla cut a third of memory from Optimus's brain — aakashgupta · 2026-11-02