Scaling robotic manipulation demands both predictive models of how the world evolves under action and rich sensing of physical contact. In this talk, I will present two complementary research directions that address these challenges. First, inspired by human intuitive physics, I introduce structured world models that incorporate physical priors through particle- and graph-based neural dynamics, as well as action-conditioned video prediction for learning predictive visual dynamics directly from data. These models enable model-based planning and control across a wide range of rigid, deformable, articulated, and granular objects, and support long-horizon, contact-rich manipulation. They also facilitate the construction of neural and physics-informed digital twins for scalable data generation, policy iteration, and evaluation. Second, I will present our work on scalable tactile sensing, from uncovering principles of human grasping with dense tactile gloves to developing flexible, low-cost tactile arrays and portable visuo-tactile grippers. Combined with simulation and large-scale real-world data collection, these tactile systems enable robust learning and improved sim-to-real transfer for tasks involving visual occlusion, fragile objects, and complex physical interactions. Together, these efforts highlight key ingredients for scaling robotic manipulation toward greater generality, robustness, and physical competence, laying the groundwork for physically grounded foundational robotic models.
Bio: Yunzhu Li is an Assistant Professor of Computer Science at Columbia University. Before joining Columbia, he was an Assistant Professor at UIUC CS and spent time as a Postdoc at Stanford, collaborating with Fei-Fei Li and Jiajun Wu. Yunzhu earned his PhD from MIT under the guidance of Antonio Torralba and Russ Tedrake. His work has been recognized with the Best Paper Award at ICRA, the Best Systems Paper Award, and as a Finalist for the Best Paper Award at CoRL. He is also a recipient of the AAAI New Faculty Highlights, the Sony Faculty Innovation Award, the Amazon Research Award, the Adobe Research Fellowship, and the First Place Ernst A. Guillemin Master’s Thesis Award in AI and Decision Making at MIT. His research has been published in top journals and conferences, including Nature and Science, and featured by major media outlets such as CNN, BBC, and The Wall Street Journal.
