Bridging Simulation and Reality for Robot Dexterity

Title: Bridging Simulation and Reality for Robot Dexterity

Abstract: While reinforcement learning has spurred breakthroughs enabling robust and dynamic robot locomotion, achieving similar success in dexterous manipulation has proven far more challenging. This talk addresses the complexities inherent in tasks requiring precise, forceful, and contact-rich interactions, such as tool use. We will begin with robust grasping, discussing our recent work building and using large-scale, diverse datasets to achieve reliable grasping with dexterous hands on physical hardware. Moving towards more dynamic tasks, we will explore how sampling-based Model Predictive Control (MPC), coupled with high-fidelity simulation, can enable sophisticated in-hand manipulation. A central theme will be the critical role, and current limitations, of simulation for fine-grained contact dynamics. To this end, we will discuss some recent work on improving dynamics modeling by training generative models with real-world data to model complex dynamics like a walking humanoid. Throughout the seminar, I will present experimental results from real-world robots, emphasizing the practical lessons learned in deploying these manipulation capabilities on physical robots.

Bio: Preston Culbertson is an incoming Assistant Professor of Computer Science at Cornell University, starting in Fall 2025. His research focuses on closing the gap between robotic capabilities and real-world complexity, developing robots that move, manipulate, and adapt with true robustness. Drawing on ideas from machine learning, computer vision, and control theory, his work explores adaptable strategies for dexterous manipulation and dynamic locomotion. Preston is currently a Research Scientist at the Robotics and AI Institute, and was previously a postdoctoral scholar at Caltech. He earned his Ph.D. in Mechanical Engineering from Stanford in 2022.