Hybrid seminar at GERAD and on Zoom.

Robotic motion planning is increasingly required to operate in uncertain, dynamic, and partially observed environments. In such settings, the classical formulation of planning as a fixed optimization problem becomes insufficient: the objective function may be difficult to specify, the environmental model and the sensor measurements may be noisy, and real-time computation must be performed under uncertainty and safety constraints.

In this talk, I will present a research program that connects inverse optimal control, robotic perception, and optimization-based planning. The central idea is to move from manually specified planning formulations toward planning systems that can learn task objectives from data, construct feedback from perception and SLAM, and solve motion planning problems robustly and efficiently. I will introduce our work on inverse optimal control for learning latent objectives and behavioral trade-offs, simultaneous localization and mapping that provides pose and environmental feedback, and optimization methods for safe trajectory generation under uncertainty. I will also discuss applications to intelligent vehicles, articulated robotic systems, and rehabilitation and assistive robotic platforms.

The broader goal is to develop planning and control methods that are not only mathematically grounded, but also deployable in real robotic systems. The talk will conclude with open problems on objective identifiability, uncertainty-aware planning, and the integration of learning, control, and perception for autonomous robots.

Speaker Bio : Han Zhang received his B.Eng. and M.Sc. degrees from the Department of Automation at Shanghai Jiao Tong University in 2011 and 2014, respectively, and his Ph.D. degree from KTH Royal Institute of Technology in Sweden in 2019. He is currently an Associate Professor at the School of Automation and Intelligent Sensing, Shanghai Jiao Tong University.

His research lies at the intersection of control theory, optimization, statistical learning, and robotics, with a focus on inverse optimal control, trajectory planning, robotic perception, system identification, and their applications to intelligent robotic and autonomous systems. He has led two projects funded by the National Natural Science Foundation of China and participated in two National Key R&D Program projects. His publications include papers in leading control and robotics venues, including Automatica and IEEE Transactions.