EgoZero: Robot Learning from Smart Glasses
Authors: name: Vincent Liu, name: Ademi Adeniji, name: Haotian Zhan, name: Raunaq Bhirangi, name: Pieter Abbeel, name: Lerrel Pinto

Despite recent progress in general purpose robotics, robot policies still lag
far behind basic human capabilities in the real world. Humans interact
constantly with the physical world, yet this rich data resource remains largely
untapped in robot learning. We propose EgoZero, a minimal system that learns
robust manipulation policies from human demonstrations captured with Project
Aria smart glasses, and zero robot data. EgoZero enables: (1)
extraction of complete, robot-executable actions from in-the-wild, egocentric,
human demonstrations, (2) compression of human visual observations into
morphology-agnostic state representations, and (3) closed-loop policy learning
that generalizes morphologically, spatially, and semantically. We deploy
EgoZero policies on a gripper Franka Panda robot and demonstrate zero-shot
transfer with 70% success rate over 7 manipulation tasks and only 20 minutes of
data collection per task. Our results suggest that in-the-wild human data can
serve as a scalable foundation for real-world robot learning - paving the way
toward a future of abundant, diverse, and naturalistic training data for
robots. Code and videos are available at https://egozero-robot.github.io.