Yuke Zhu: Toward Generalist Humanoid Robots - Recent Advances, Opportunities, and Challenges
Yke Zhu, University of Texas at Austin
Attendance Link: https://tinyurl.com/robotsem-fall-2025
If you’re interested, you’re welcome to join Yuke for lunch at Blend Cafe at 12 PM. Please let Yifan (yifanhou@stanford.edu) know if you plan to join.
Abstract
In an era of rapid AI progress, leveraging accelerated computing and big data has unlocked new possibilities to develop generalist AI models. As AI systems like ChatGPT showcase remarkable performance in the digital realm, we are compelled to ask: Can we achieve similar breakthroughs in the physical world — to create generalist humanoid robots capable of performing everyday tasks? In this talk, I will outline our data-centric research principles and approaches for building general-purpose robot autonomy in the open world. I will present our recent work leveraging real-world, synthetic, and web data to train foundation models for humanoid robots. Furthermore, I will discuss the opportunities and challenges of building the next generation of intelligent robots.
Bio
I am an Associate Professor in the Department of Computer Science at the University of Texas at Austin and the director of the Robot Perception and Learning (RPL) Lab. I am also a Director and Distinguished Research Scientist at NVIDIA Research, where I co-lead the Generalist Embodied Agent Research (GEAR) group.
My goal is to build algorithms and systems for autonomous robots and embodied agents that reason about and interact with the real world. My research lies at the intersection of robotics, machine learning, and computer vision. I focus on developing methods and principles of perception and decision-making to realize general-purpose robot autonomy in the wild.
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.
Covid-19 related instructions: We recommend wearing a well-fitted, high-quality face covering inside the classroom.