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Fourth Seminar in 2023-2024 Seminar Series

We're excited to continue our virtual seminar series for the TC on Model-Based Optimization for Robotics.

We'll have a talk from Ludovic Righetti from New York University (NYU) at 9AM EST March 14th 2024 (Thursday). Please find the flyer with all the details at this link.

Speaker: Ludovic Righetti (New York University (NYU))

Title: Learning complex behaviors with nonlinear MPC

Abstract: Nonlinear model predictive control (MPC) is a reliable technology to generate a variety of robotic behaviors, from flying robots to humanoids. While MPC is a rigorous framework to generate, in principle, any kind of behavior from a single algorithm, major limitations remain. For example, current approaches do not allow easy inclusion of multi-modal sensing, especially visual and force feedback, and algorithms struggle to optimize in real-time multi-contact behaviors necessary for complex manipulation or locomotion. On the other hand, learning-based methodologies, which heavily rely on offline compute, do not seem to struggle with these issues. In this talk, I will present our recent work tackling those problems with a particular eye towards unifying learning and numerical optimal control. First, I will argue for the benefits of “textbook” numerical optimization methods to develop reliable solvers. Then I will discuss how to include multi-modal sensing and accelerate the computation of multi-contact behaviors through a mixture of offline compute (learning) and online optimization (MPC). I will then show how this can lead to improved performance for movement generation in the context of locomotion and manipulation and discuss on-going challenges.

Date: Thursday, March 14th 2024

Time: 09:00-10:00 AM EDT


More details on upcoming seminars (and video links for past ones) can be found here.