Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?
Egocentric human data provides a scalable source of experience for robot learning, but varies substantially in human-robot alignment, behavioral coverage, and available supervision. Existing work shows favorable scaling with increasing human data, but it remains unclear which data properties drive downstream robot gains and how to use such data throughout the training pipeline. We present a systematic study of egocentric human data with different alignment and supervision under a unified world-action model framework. With the model backbone fixed, we disentangle the effects of human-robot alignment, data duration and task diversity, action supervision, and data usage strategies. We find t...
| Authors | Zhihao Sun, Liu Liu, Xinjiang Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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