Armand Jordana

LAAS-CNRS (Toulouse, France)

Monday, January 19, 2026, 10:00 - 11:00

SR 01-012

In robotics, nonlinear Model Predictive Control (MPC) has emerged as a promising tool to generate complex motions while enabling online adaptation of the robot behavior as the environment changes. However, the lack of efficient computational methods hindered its widespread deployment on real hardware. In practice, MPC formulations and computational methods are often simplified to obtain real-time capable controllers. In this talk, I will present efficient numerical methods to fully leverage the promises of MPC on robots by ensuring safe and globally optimal plans while being aware of the uncertainty resulting from the partial sensing of the environment. Finally, I will discuss recent results showcasing connections between gradient-free optimization techniques and Reinforcement Learning.


Speaker's homepage: https://ajordana.github.io/