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.

From the abstract. Our summary is in progress.