ABSTRACT: Large language models like ChatGPT have demonstrated that generalist foundation models can effectively tackle long-horizon tasks by training on vast amounts of internet text data. The next wave of AI development is expected to be driven by large-scale data from the physical world, such as those generated by autonomous vehicles and the healthcare industry. A key challenge in deploying highly intelligent agents at scale in the physical world is ensuring their safety. In this talk, I will present our efforts to establish Trustworthy Physical AI Agents to support this crucial transformation. I will explore the challenges of ensuring safety and generalization in trustworthy AI development and discuss potential solutions, including rare event analysis, safe reinforcement learning, critical digital twins and sim2real, and causal reasoning. Additionally, I will introduce applications of our work in self-driving, assistant robots, and medical companion robots.

BIOSKETCH: Ding Zhao is an Associate Professor and Dean’s Early Career Fellow at Carnegie Mellon University, where he leads the Safe AI Lab. His research focuses on developing Trustworthy Physical AI systems for high-stakes applications at scale. Zhao collaborates with leading industry partners, including Google, Nvidia, Amazon, Apple, Microsoft, IBM, Ford, Uber, Bosch, Toyota, and Rolls-Royce. He has grants from NSF, DOT, DOE, and DARPA and published over 120 papers in top venues such as ICML, NeurIPS, ICLR, AISTATS, CoRL, ICRA, IROS, and Nature Communications. Zhao has received numerous awards, including CMU Dean’s Early Career Fellow Professorship, Provost’s Inclusive Teaching Fellows Award, National Science Foundation CAREER Award, MIT Technology Review 35 Under 35 Award in China, George N. Saridis Best IEEE Transactions Paper Award, George Tallman Ladd Research Award, Struminger Teaching Award, Ford University Collaboration Award, Qualcomm Innovation Award, Carnegie-Bosch Research Award, and various industrial fellowship awards from Google DeepMind, Adobe, Toyota, and Bosch. His work has garnered attention from media outlets such as the New York Times, Forbes, TIME, IEEE Spectrum, Popular Science, Telegraph, and Wired.