Plenary Talk:  Robust Robot Behavior Through Online Optimization

Speaker:  Prof. Preston Culbertson, Cornell University

Date:  Friday, January 30, 2026

Abstract: Much recent progress in robotics has been driven by learned policies trained offline in simulation or from large datasets, enabling impressive demonstrations in locomotion and manipulation. Despite this success, a key challenge remains: robots must operate in the “long tail” of environments, where objectives, dynamics, and constraints differ from those seen in training, often causing fixed policies to break down. In this talk, online optimization serves as a unifying approach for robust robot behavior: actions are adapted at runtime by solving optimization problems whose costs and constraints are specified at execution time, with learning used to provide models, priors, or uncertainty estimates that make this adaptation tractable. I will present real-world examples from humanoid locomotion, whole-body manipulation, and in-hand dexterous manipulation that demonstrate reliable, risk-sensitive behavior in contact-rich and difficult-to-model environments.

Bio: Preston Culbertson is an Assistant Professor of Computer Science at Cornell University. Prior to joining Cornell, he was a research scientist at the Robotics and AI Institute and a postdoctoral scholar at the California Institute of Technology. He received his PhD and MS from Stanford University and his BS from the Georgia Institute of Technology, all in mechanical engineering. His research draws on optimization, control theory, and machine learning to develop robotic systems that remain reliable when models, sensing, or hardware are imperfect.