Events

Research Talk Physics-Informed Learning for Robot Motion Planning and Control
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Wednesday, July 15, 2026, 10:00am - 11:00am

Speaker: Ahmed Qureshi, Computer Science, Purdue University

Bio

Ahmed Qureshi is an Assistant Professor in the Department of Computer Science at Purdue University, where he directs the Cognitive Robot Autonomy and Learning (CoRAL) Lab. His research pursues a physics-first philosophy for robot motion learning: rather than relying on large expert demonstrations or trial-and-error interaction, his group develops methods that embed the governing laws of physics directly into learning algorithms. This approach has produced self-supervised methods that train in minutes, require no expert annotation, and plan in near real-time across high-dimensional, manipulation, and unknown environments. His broader research spans scalable motion planning, dexterous manipulation, active perception, and multi-agent task and motion planning. Dr. Qureshi's work has been recognized with spotlight and best paper awards at top academic venues. He serves as an Associate Editor for IEEE Transactions on Robotics and IEEE Robotics and Automation Letters, and received the Outstanding Associate Editor Award from RA-L in 2024. He has served on the program committees of RSS, ICRA, IROS, and CoRL. He earned his B.S. in Electrical Engineering from NUST, M.S. from Osaka University, and Ph.D. in Intelligent Systems, Robotics, and Control from UC San Diego.

Website: https://qureshiahmed.github.io/

Location : SPR-403, at 1 Spring Street, Downtown New Brunswick

Committee: 

Event Type: Research Talk

Abstract: Modern robot motion learning often relies on large datasets and expensive expert demonstrations to learn structure that physics can already provide. This talk discusses an alternative approach: instead of asking neural networks to rediscover physical principles from data, PDE-based priors can be embedded directly into the learning process. The talk focuses on motion planning through continuous value functions governed by the Eikonal PDE, a special case of Hamilton-Jacobi equations that captures shortest-path and minimum-time behavior. This formulation enables self-supervised robot motion learning without relying on expert trajectories, exhaustive graph search, or trial-and-error interaction. The resulting methods train quickly, generalize across environments, and infer motion plans in near real time. The talk also shows how these ideas scale to high-dimensional systems and constraint-rich manipulation tasks, and how Eikonal priors can provide a motion-aware mapping representation that is more directly useful for planning than standard occupancy grids or signed distance fields. Finally, it discusses how incorporating these priors into reinforcement learning improves data efficiency and scalability. Together, these results suggest that physics can serve as a useful foundation for learning robot motion, with data used to adapt and extend these priors rather than replace them.

Organization: 

Contact  Professor Kostas Bekris

Zoom info:https://rutgers.zoom.us/j/97790581415?pwd=XTCaNAkCjJt8HiyQkKDZ6zH8dw5rXY.1&from=addon
Meeting ID: 977 9058 1415
Passcode:299421