LCSR Seminar: Self-Supervised Robot Motion Learning via Physics-based PDE Priors

Description

Ahmed Qureshi, an assistant professor in the Department of Computer Science at Purdue University, will give a talk titled "Self-Supervised Robot Motion Learning via Physics-based PDE Priors" for the Laboratory for Computational Sensing + Robotics. Qureshi's group pursues fundamental and applied research in robot motion planning and control, with the goal of developing methods that can leverage the laws of physics to plan and act in real time with minimal or no expert demonstrations. His research spans scalable and efficient motion planning, dexterous manipulation, active perception, and multi-agent task and motion planning.

Abstract:

This talk explores how partial differential equation (PDE)–based physics priors can provide a foundation for scalable and generalizable algorithms in robot motion learning. Rather than searching over discrete graphs or samples, it formulates and learns the solution to the motion-planning problem as a continuous value function governed by Hamilton–Jacobi (HJ) PDEs. These methods enable self-supervised value-function learning without reliance on expert trajectories or trial-and-error interaction. The learned value functions yield fast inference of motion plans and demonstrate strong scalability across complex, high-dimensional, and constraint-rich navigation and manipulation tasks. The talk also introduces an HJ PDE–derived mapping representation that unifies perception and planning: unlike occupancy grids or signed distance fields, it encodes motion-feasible geometry in a form naturally structured for continuous decision-making. Together, these developments outline a unified, numerically grounded framework for robot motion planning and control through the lens of physics-informed learning.