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Center for Autonomy
Enabling high-impact research in autonomous system design
About the Center for Autonomy
Located at the University of Illinois at Urbana-Champaign, the Center for Autonomy will enable high-impact research and develop new educational programs for students and professionals. The Center will play an important role in designing innovative systems that can function autonomously, or without human intervention, in a safe and reliable way.
From self-driving cars to intelligent robotic assistants to remote surgical systems, autonomous technology will revolutionize the way we live, work, and play. In order to enable this revolution, however, advancements in foundational research and workforce development must first take place to provide assured and certified-safe performance.
Application areas include:
UPCOMING SEMINAR
Physics as the Backbone of Dexterity: Scalable Contact Simulation, Contact-Aware Control, and Physics-Grounded Learning
Friday, March 13, 2026
2:00pm Central Time
Speaker: Waxin Jin (ASU)
Live Zoom Event
Dexterous manipulation is fundamentally governed by physical contact. Rather than relying solely on larger datasets and end-to-end policies, I argue that contact-rich robot dexterity can be tackled more effectively by explicitly leveraging contact structure: where to interact, how contact evolves, and how motion unfolds under contact dynamics. In this talk, I will present a physics-grounded view of robotic dexterity spanning contact simulation, control, world model learning, and human feedback. I will first introduce complementarity-free analytical contact modeling and simulation, which enable closed-form contact resolution, differentiability, and real-time (100 Hz) contact-implicit MPC for dexterous manipulation. Building on this foundation, I will present ComFree-Sim, a GPU-parallel analytical contact physics engine that achieves linear runtime scaling with contact density and significantly improves throughput in dense contact-rich simulation and control. I will then present a contact-interfaced hierarchical framework for geometry-aware long-horizon dexterous manipulation, which decomposes the problem into contact-intention learning and robust contact execution. This structure enables data-efficient learning, robust performance, and zero-shot sim-to-real transfer. Next, I will discuss contact-aware world model learning, a differentiable vision-to-physics pipeline from sparse, contact-rich videos that unifies rendering and contact physics priors. Finally, I will briefly describe our recent work on robust learning from human feedback, including robust reward alignment and direct fine-tuning of diffusion policies under corrupted feedback.
Wanxin Jin is an Assistant Professor in the School for Engineering of Matter, Transport and Energy at Arizona State University. His research lies at the intersection of robotics, control, and machine learning, with a focus on contact-rich dexterous manipulation, physics-grounded robot learning, and human-centered autonomy. Prior to joining ASU in 2023, he was a postdoctoral researcher at the GRASP Laboratory at the University of Pennsylvania. He received his Ph.D. in Aeronautics and Astronautics from Purdue University in 2021. His work has appeared in leading robotics and machine learning venues, including T-RO, IJRR, RSS, ICRA, ICML, and NeurIPS. He is an Associate Editor for IEEE Robotics and Automation Letters (RA-L)