A Million Hours of Human Video, Zero Robot Frames

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A Million Hours of Human Video, Zero Robot Frames<br>Dyna Robotics claims it trained a robot policy using a million hours of first-person human video, without any robot data.<br>Jay Chia<br>Aug 13, 2026

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Dyna’s scaling law and what you can verify

Dyna trained one model at 1K, 10K, 100K, and 1M hours of human video and measured what each budget bought. Before any robot data went in, they tested it on 39 manipulation tasks across two bimanual YAM platforms. Every score improved as the pretraining video grew. On a separate set of 14 tasks, this time with robot post-training, the aggregate score climbs 20%, 28%, 45%, 53% up the same ladder. The result people will quote is Lockbox Key Turning, which sits at 0% at every budget through 100K hours and reaches 90% at a million.<br>DYNA-2 is a world-action model. It uses a video-diffusion backbone and a mixture of transformers. The model denoises future video and future actions, either together or separately. The pretraining set is over a million hours of human video. It shows people cooking, tidying, folding, and assembling. The footage comes from Dyna’s data partners and its own operation. Annotation combines 3D hand-pose tracking with pseudo-action labels. Those labels come from wrist poses and grasp signals. No robot frames go in at any point.

Two things to keep straight. Most coverage ran “20% to 80-90%” as the scaling headline. That figure belongs to one manufacturing task, and the 14-task aggregate tops out at 53%. Reporting also settled on “13 minutes” of fine-tuning data for the two-hand bottle-cap task, where the report says roughly 10.<br>There are no weights, no code, no dataset, and no arXiv posting. The technical report is on Dyna’s own site. Dyna designed, ran, and scored the 39 held-out tasks and the 14 post-training benchmarks. It also chose the evaluators for the comparison against DYNA-1. One piece of external grounding does exist. 27 of the 39 evaluation tasks come from the public xdof ABC dataset rather than being built in-house. That makes a partial outside replication possible. If the curve holds up in someone else’s hands, that is a bigger story than the launch was.<br>Thanks for reading Topic Queue! Subscribe for free to receive new posts and support my work.

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Research

The same checkpoint scores 84% or 8%, depending on how you ask

LeWorldModel is a JEPA world model from a team that includes Yann LeCun. It gets cited as evidence that latent world models are ready to plan with. An independent reimplementation rebuilt it for about $25 and matched the published TwoRoom number. Then it found the number is not what it appears to be. Four training conventions decide whether the model converges, and none of them appear in a released config file. Anyone following the published configs gets a predictor that never trains. Worse, the released material disagrees with itself about how to evaluate. The paper’s appendix and the repository config specify different goal offsets and step budgets. On the authors’ own checkpoint, those two protocols score 14% and 84%. Change nothing but how the goal is constructed, and the same checkpoint drops to 8% across fifty identical episodes. Every one of those numbers describes the same weights. None of this makes LeWorldModel a bad model. It does mean a published planning score reflects the evaluation protocol as much as the weights. Comparing two checkpoints means running both yourself, under one protocol you control.<br>GAUGE measures physics engines against reality, and none of them win

GAUGE evaluates Isaac Sim, Genesis, and Newton across 14 task families. It scores them against real measured trajectories, with calibrated physical metadata and uncertainty annotations. Coverage spans rigid bodies, flexible cables, textiles and volumetric deformables. No engine is faithful across all of them. The largest errors cluster in impulsive contact, fast textile motion, and volumetric deformation. On the generative side, six image-to-video models can produce trajectories that match the expected equation form. The same trajectories still get accelerations, momentum transfer, and oscillation timing wrong. The practical read is that engine choice is task-dependent. If your tasks involve impulsive contact or cloth, the engine you standardized on may be the wrong one for them.

RynnValue derives reward labels from timestamps

Reward models are the expensive part of RL post-training, because someone has to sit and label preferences. RynnValue uses a label you can read off a timestamp. Every frame is tagged with how much time is left before the instructed task finishes. The authors call this temporal distance. Nobody ranks anything by hand. That scales to over 7,000 hours and roughly 3M instruction-conditioned clips, with no preference or progress annotation at all. Three tricks stop the model from gaming the label. The useful one is shuffling the temporal order...