EgoScale：利用多样化第一人称人类数据扩展灵巧操作

EgoScale：利用多样化第一人称人类数据扩展灵巧操作Abstract 摘要Human behavior is among the most scalable sources of data for learning physical intelligence,yet how to effectively leverage it for dexterous manipulation remains unclear.While prior work demonstrates human-torobottransfer in constrained settings,it is unclear whether large-scale human data can support fine-grained, high-degree-of-freedom dexterous manipulation. We present EgoScale,a human-to-dexterous-manipulation transfer framework built on large-scale egocentric human data.We train a Vision-Language-Action (VLA) model on over20,854^ { 2 0 , 8 5 4 }