Dyna Robotics’ DYNA-2 world-action model scales on 1M hours of human video
Confirmed
Importance: major (4 of 5)Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- Major (4 of 5)
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 Astra102 days after its cutoff
- Claude Opus 5.541 days after its cutoff
- Gemini 4 Hydrogen132 days after its cutoff
- Grok 4.771 days after its cutoff
None of these four assistants can know about it. The closest, Claude Opus 5.5, stops 41 days before it.
Key facts
- Pretraining: 1M+ hours of egocentric human video (~170 years of waking experience)
- Architecture: video-diffusion world-action model jointly denoising future video and action chunks
- Customer deployment: 87% quality pass rate zero-shot vs 46% for DYNA-1; 1.55x more successes
- One-step distilled video generation, 90x faster than teacher; bottle-cap opening from 10 min of robot data
What happened
Dyna, whose DYNA-1 already runs in production in hotels, restaurants and laundromats, showed that robot performance improves predictably with more human video, with no plateau up to 1M hours. Dyna calls it the first scaling law across the embodiment gap.
Why it matters
Human video is far cheaper to collect than robot teleoperation. Together with Figure’s Helix 2.5 and Generalist GEN-1, DYNA-2 suggests 2026 is the year robot learning found a scalable data source. Claims are company-reported.
Sources
3 sources from 3 sites. Numbers match the chips in the text.
3 sources: 2 primary, 1 press
Primary
- Dyna: DYNA-2 — A 1-Million-Hour Scaling Law for World-Action Modelsdyna.co, official
- PR Newswire: Dyna Robotics unveils DYNA-2prnewswire.com, official
Press
- MarkTechPost: Dyna Robotics introduces Dyna-2marktechpost.com, press
Changes
- Filed