HuRo turns five human-video sources into about 142 million robotized frames, lifting real-robot OOD completion from 34.9% to 72.2% in ALLEX VLA pretraining
HuRo introduces a robotization pipeline that converts heterogeneous egocentric human videos into robot-aligned observations and action trajectories, builds the HuRo dataset of about 142 million frames from five sources (EgoDex, EgoVerse, Ego4D, Ego10K, and EPIC-Kitchens), and uses it to pretrain VLA policies on the ALLEX bimanual dexterous robot: as robotized data increases, average completion across four real-world manipulation tasks rises from 51.5% without pretraining to 80.3% with the full dataset, with ID completion from 68.1% to 88.4% and OOD completion from 34.9% to 72.2%.