UMD computer science professor Furong Huang launches HumanEgo to train robot policies from 30 minutes of egocentric video

FHFurong Huang@furongh

Excited to share our HumanEgo 🧝‍♂️ project, led by Leo 🦁! HumanEgo is a glimpse of the closed-loop ecosystem robotics needs: human data at scale → transferable robot policies → real-world deployment → feedback-driven improvement. This connects naturally with EgoScale at NVIDIA GEAR, a project I’m part of, led by my former student now at NVIDIA GEAR. EgoScale scales the same idea: using large-scale egocentric human video to unlock dexterous robot intelligence. Together, they point to a broader thesis I deeply believe in: Humans are the scalable data source. Robots are the deployment engine. Feedback is the path to continually improving robotics foundation models. More of my vision here 👉 EgoScale 👉https://research.nvidia.com/labs/gear/egoscale/

Z(Zhi (Leo) Wang@TX_Leo_Wang

1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: http://humanego-ai.github.io 📄 Paper: http://arxiv.org/pdf/2605.24934 💻 Code: http://github.com/TX-Leo/HumanEgo 📹 Video: http://youtu.be/pdL46diijuY 🧵 1/n