Majid Khadiv (TU München), “Optimal control and learning for contact-rich robotics”

The past few years have witnessed significant progress in the field of legged locomotion and manipulation. This is mainly due to the availability of high-performance hardware as well as development of algorithms that scale to high-dimensional, hybrid and under-actuated systems. In this talk, I will present my recent research efforts, mainly on the algorithmic side, on developing efficient predictive controllers that can be complemented with supervised/reinforcement learning for real-time execution in the real world. I will also share my perspective on the open problems that we still need to solve to have functional humanoid robots in the real world.

Majid Khadiv is an assistant professor in the school of Computation, Information and Technology (CIT) at TUM. He leads the chair of AI Planning in Dynamic Environments and is also a member of the Munich Institute of Robotics and Machine Intelligence (MIRMI). Prior to joining TUM, he was a research scientist at the Empirical Inference Department at the Max Planck Institute for Intelligent systems. Before that he was a postdoctoral researcher in the Machines in Motion, a joint laboratory between New York University and Max Planck Institute. Since the start of his PhD in 2012, he has been performing research on motion planning, control and learning for legged robots ranging from quadrupeds, lower-limb exoskeleton up to humanoid robots.

This talk will take place as part of SCIoI member Svetlana Levit’s seminar “Selected Topics in Robot Learning,” which explores how advances in machine learning are helping robots operate in new environments, learn new behaviors, and adapt to changing conditions.

Image generated with DALL-E by Maria Ott.