Egocentric human video offers a scalable source of manipulation priors for robot learning, yet translating raw data scale into generalizable robot policies demands rigorous data curation and training
Egocentric human video offers a scalable source of manipulation priors for robot learning, yet translating raw data scale into generalizable robot policies demands rigorous data curation and training recipes. We introduce Open-HumanScale, the first fully open recipe for scaling egocentric pretraining to 50,000 hours, anchored by Open-HumanScale-50K---a curated egocentric video corpus densely annotated with hand motion and natural language. Leveraging this corpus, we systematically investigate pure human pretraining via closed-loop simulation evaluations and uncover an empirical log-linear scaling law linking pretraining data hours to downstream robot success rates. Grounded in these…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Open-HumanScale: An Open Recipe for Scaling Human and Robot Experience for Embodied Foundation Models was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 26 checks on 3 different days. Its best position was #11 of 30, reached on Nov 8, 07:28 UTC. It is #11 right now, climbing from #13 to #11 over the last 6 checks.
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Yes. As of Nov 8, 15:28 UTC it is #11, climbing from #13 to #11 over the last 6 checks.
#11 on Hugging Face · Daily papers, on Nov 8, 07:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 6, 2026 and has been on the list on 3 days (26 checks) since.