Title: Robot Learning from Human Experience: Science and Scaling

Speaker: Prof. Danfei Xu (Georgia Tech) (https://faculty.cc.gatech.edu/~danfei/)

Location: Gates B3 (link)

Attendance Link: https://tinyurl.com/robosem-spr-2026

Time: Friday Apr 10th, 3:00-4:00PM

Abstract: Modern AI advances by transferring knowledge from humans to machines at scale. Vision and language models learn from vast Internet data, but robot learning still relies heavily on slow, labor-intensive teleoperation. Recently this assumption has begun to shift: growing industrial efforts are collecting large amounts of human experience data to scale robot performance. As large-scale data collection becomes increasingly feasible, the central challenge shifts to understanding how robots can learn from human behavior. In this talk, I argue that human-to-robot transfer can be understood as two coupled problems: extracting priors about physical intelligence from human experience, and grounding those priors into a robot’s embodiment. I will revisit several of our recent works through this lens, showing how egocentric human data enables scalable learning of manipulation priors, while representation learning and cross-embodiment transfer address the grounding challenge. I will also discuss recent results showing emergent human-to-robot transfer from large-scale human pretraining, as well as evidence that learning across diverse robot embodiments can further improve transfer. Finally, I will introduce EgoVerse, an ecosystem for robot learning from embodied human data, and discuss how collaborative platforms can enable both rigorous science and organic data growth. I will conclude with future directions toward more human-centered robots that better understand human intent and collaborate naturally with people.

Please visit https://stanfordasl.github.io/robotics_seminar/ for this quarter’s lineup of speakers. Although we encourage live in-person attendance, recordings of talks will be posted also.

If you’re interested, you’re welcome to join Danfei for lunch at Blend Cafe at 12 PM. Please let Dian Wang (dianwang@stanford.edu) know if you plan to join.