title: EgoZero: Robot Learning from Smart Glasses
summary: 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.
paper_authors._id: 683534ac4ab4471ff376dfca
paper_authors.name: Vincent Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:50:29.968Z
paper_authors._id: 6606d5370c87906d8e287fd6
paper_authors.avatarUrl: /avatars/b01d9f9a2f1b3f3d18944f48e36cb54c.svg
paper_authors.fullname: vincent liu
paper_authors.type: user
paper_authors.user: vincentjliu
paper_authors._id: 683534ac4ab4471ff376dfcb
paper_authors.name: Ademi Adeniji
paper_authors._id: 683534ac4ab4471ff376dfcc
paper_authors.name: Haotian Zhan
paper_authors._id: 683534ac4ab4471ff376dfcd
paper_authors.name: Raunaq Bhirangi
paper_authors._id: 683534ac4ab4471ff376dfce
paper_authors.name: Pieter Abbeel
paper_authors._id: 683534ac4ab4471ff376dfcf
paper_authors.name: Lerrel Pinto
paper_title: EgoZero: Robot Learning from Smart Glasses
paper_summary: 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.
paper_ai_summary: EgoZero learns robust manipulation policies for robots using in-the-wild human demonstrations and zero robot data, enabling zero-shot transfer across diverse tasks.
paper_ai_keywords: EgoZero human demonstrations Project Aria smart glasses robot-executable actions egocentric morphology-agnostic state representations closed-loop policy learning zero-shot transfer gripper Franka Panda robot