Open-HumanScale: An Open Recipe for Scaling Human and Robot Experience for Embodied Foundation Models
The paper introduces Open-HumanScale, an open recipe for scaling egocentric human video pretraining to 50,000 hours, anchored by the Open-HumanScale-50K corpus with hand-motion and language annotations.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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The paper introduces Open-HumanScale, an open recipe for scaling egocentric human video pretraining to 50,000 hours, anchored by the Open-HumanScale-50K corpus with hand-motion and language annotations. The authors report an empirical log-linear relation between pretraining hours and downstream robot success, and develop HumanWAM-Vanilla (human-only pretraining) and HumanWAM-Goal (joint human-robot pretraining with visual goals). Reported results include 92.08% and 68.88% success on RoboTwin 2.0 Clean2Clean and Clean2Random for HumanWAM-Vanilla, and 92.70% and 84.60% for HumanWAM-Goal. Corpus, pipelines and checkpoints are said to be released.
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Source claims not independently checked; results are on simulation benchmarks (RoboTwin 2.0, RoboDojo) with limited physical-robot evidence described in the text.
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Scaling egocentric pretraining to 50,000 hours is reported to yield a log-linear relation with downstream robot success rates.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseHumanWAM-Vanilla, pretrained only on human video, reports 92.08% on RoboTwin 2.0 Clean2Clean and 68.88% on Clean2Random.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseHumanWAM-Goal, using joint human-robot pretraining, reports 92.70% Clean2Clean and 84.60% Clean2Random on RoboTwin 2.0.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseLimits from the stored brief
Source claims not independently checked; results are on simulation benchmarks (RoboTwin 2.0, RoboDojo) with limited physical-robot evidence described in the text.
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Why this may matter
If the reported scaling trend and benchmark gains hold, they suggest large unlabeled human video could be a practical source of manipulation priors for robot foundation models, with open data and checkpoints enabling reproduction.
Limits and open questions
Source claims not independently checked; results are on simulation benchmarks (RoboTwin 2.0, RoboDojo) with limited physical-robot evidence described in the text.
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