Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?
This study investigates how different properties of egocentric human data affect robot learning using a unified World-Action Model framework. It found that aligned demonstrations improve generalization, while video-only supervision is effective without action labels. The value of this data depends on its distribution coverage and how it's integrated across the entire training pipeline, not just its sheer volume.