Paper page - HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining

Source: https://huggingface.co/papers/2606.20521 Published on Jun 18

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Submitted byhttps://huggingface.co/yfdeng10

yfdengon Jun 19

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Abstract

Egocentric human video can effectively replace teleoperated robot trajectories for embodied model pretraining, achieving better performance with reduced data collection costs.

Embodied foundation modelsare expected to benefit fromdata scalinglike large language models, but face a much tighter data bottleneck.Teleoperated real-robot trajectoriesremain the dominantpretrainingsource due to their precise action supervision and embodiment alignment, yet their scalability is limited by high collection cost, acquisition difficulty, and low behavioral andenvironmental diversity. These limitations have sparked interest inegocentric human videoas a scalable, substantially lower-cost, and more diverse alternative for embodied modelpretraining. However, its effectiveness compared to teleoperated real-robot data remains underexplored. To address this question, we conduct a systematic study comparingegocentric human videoandteleoperated real-robot trajectoriesaspretrainingdata sources forembodied foundation models, under fixed post-training and validation protocols. Surprisingly, we find that egocentric data, when processed through a carefully designed filtering andlabeling pipeline, is not merely a viable substitute for modelpretrainingbut can lead to superior performance. With the same amount ofpretrainingdata, models pretrained on egocentric data achieve a 24% lower validation loss on real-robotaction prediction, as well as 52.5% and 90% higher success rates on in-distribution and out-of-distribution real-robottask execution, respectively. This finding verifies a scalable paradigm forembodied foundation models: pretrain onegocentric human videoto learn diverse world representations, then adapt with a small amount of labeled real-robot data foraction-space alignment. We hope this study encourages broader exploration of egocentric data and offers guidance for data quality assessment before costly robot data collection.

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