Keynotes

Wolfram Burgard 

Title: Challenges to Realize Embodied AI

Abstract: To achieve Embodied Artificial Intelligence, we need robots that can robustly perceive their environments and execute actions. The key challenge is that no sensors or actuators are perfect, so robots must be able to handle the resulting uncertainty. In this presentation, I will discuss the opportunities of the probabilistic and deep-learning approaches to robotics and how they can be combined to get the best of both worlds. Finally, I will describe how we can utilize the potential of foundation models to even better deal with complex and changing real-world environments.

Bio: Wolfram Burgard is a professor of Computer Science at the University of Technology Nuremberg, where he heads the research group on Robotics and Artificial Intelligence. Before this, he was a professor of autonomous intelligent systems at the University of Freiburg, Germany. From 2019 until 2021, he was VP for Automated Driving and Machine Learning at the Toyota Research Institute in Los Altos, USA. He has published over 400 papers. He received multiple awards, including the IEEE Technical Field Award, the IEEE RAS Pioneer Award, and the Leibniz Prize of the German Research Foundation. He is a fellow of the IEEE, the AAAI, and the EurAI, and a member of the German Academy of Sciences Leopoldina and the Heidelberg Academy of Sciences and Humanities.

Russ Tedrake

Title: Learning in the factories

Abstract: How do we make large models for manipulation useful in real applications, and what value can real-world deployments bring back to the models?

We started Walden Robotics in January 2026 with the conviction that it was time to get out of the labs and into real applications. We deployed our first robot into a live production environment just two months later, and have been learning and expanding our fleet ever since. We believe that the best physical AI will be built via a continuous cycle of deployment and learning grounded in real work. And we've been testing that belief with data.

In this talk I'll discuss what we've learned about how performance scales with deployment. I'll also share some specific ways in which my own world view has changed as I've been spending more and more time in the factories and with experts who have spent their careers in mass-production manufacturing. 

Bio: Dr. Russ Tedrake is co-founder and CEO of Walden Robotics. Russ is also the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT.

Russ was formerly the Senior Vice President of Large Behavior Models at the Toyota Research Institute. He was the leader of Team MIT's entry in the DARPA Robotics Challenge. He is well known for his leadership of the open-source robotics toolkit Drake and for his online courses/textbooks: Underactuated Robotics and Robotic Manipulation.

Fei-Fei Li

Title: Atlas: A Unified World Model for Generation and Reconstruction

Abstract: In this talk, Dr. Fei-Fei Li will discuss recent advances in world modeling based on work by the research team at World Labs. In particular, she will present Atlas, an autoregressive frame generation model that unifies pixel generation and 3D scene reconstruction.

The second half of the session will feature a fireside chat between Dr. Fei-Fei Li and Dr. Lisa Su, Chair, President and CEO of AMD.

Bio: Dr. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and a Founding Co-Director of Stanford’s Human-Centered AI Institute. She served as the Director of Stanford’s AI Lab from 2013 to 2018.

She is currently the Founder CEO of World Labs, a frontier AI research and product company advancing spatial and physical intelligence. Previously, Dr. Li has served as a Vice President at Google and Chief Scientist of AI/ML at Google Cloud (2017-2018), board member or advisor in various public or private companies (notably Twitter).

Lisa Su

Bio: As chair and chief executive officer at AMD, Dr. Lisa Su has led the company's transformation into the industry's high-performance and adaptive computing leader, helping solve the world's most important challenges by delivering the next-generation of computing and AI solutions.

Prior to serving as chair and CEO, she was the chief operating officer responsible for integrating AMD's business units, sales, global operations and infrastructure enablement teams into a single market-facing organization responsible for all aspects of product strategy and execution. Prior to AMD, Dr. Su held leadership roles at Freescale Semiconductor, including chief technology officer and senior vice president and general manager of Networking and Multimedia. She spent 13 years at IBM in engineering and business leadership positions, including leading the Semiconductor Research and Development Center.

In 2026, she received the National Medal of Science, the United States' highest recognition for scientists and engineers. She is a member of the American Academy of Arts & Sciences and the National Academy of Engineering, and a recipient of the IEEE Robert N. Noyce Medal.