🤖 Expensive robot data is scarce, so why not learn from our everyday first-person videos? This work turns human hand motion into robot actions to pretrain VLA models. 📰 Title: ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining 🔗 URL: 💡 Overview ACE-Ego-0 unifies robot demonstrations with egocentric human videos (Ego4D, EPIC-KITCHENS, and more) to pretrain Vision-Language-Action (VLA) models, trained on over 6,000 hours of combined data. 🔍 Challenges Solved Robot demonstrations are costly to collect, while human videos are cheap and abundant. But the two differ in action space, embodiment structure, temporal dynamics, and supervision quality, so naively mixing them breaks training. 🛠 Methodology & Proposed Approach ・Unifies actions in head-camera coordinates with 6D rotations, treating the human hand as an end-effector ・Encodes robot URDFs into morphology tokens via a GNN to absorb structural differences ・Chunks actions by consistent physical duration instead of fixed steps for temporal alignment ・Applies a reliability-weighted loss to noisy human videos, focusing on trustworthy position channels 📊 Use Cases / Results On RoboCasa it hits 72.8% average success (vs GR00T-N1.6 at 47.6%) and ~91% on RoboTwin 2.0. On a real bimanual robot it reaches 78.3% average (π0.5: 71.7%). Strikingly, on a task with only 34 robot demos, adding 419 human video episodes lifted success from 10% to 40%, a 4x gain. #RobotLearning# #VLA#

Massive thanks to @basepilipinas and @0xmoonlight_ for for hosting such a dynamic, insightful, and high-energy experience at the 14th BYCIT 🇵🇭 Over 500+ student participants experienced @axisrobotics firsthand - directly training Physical AI through our browser-based robotics platform on their laptops. From teleoperating robotic tasks to contributing real-world data trajectories for robot learning, students got to experience how everyday users can actively participate in building the future of Robotics General Intelligence. This is exactly what Axis is about: Making robotics data generation accessible, scalable, and community-driven. Proud to be the only robotics project at the event alongside the incredible @base PH ecosystem 📷 The future of Physical AI will not be built by a few labs alone - it will be contributed by everyone. Comment which university in Phillipines you want Axis to be there 📷👇