Learning Complex Robotic Behaviors With Optimal Control

On behalf of our partners at the Bavarian AI network baiosphere, the MCML cordially invites you to the Munich AI Lectures.

Nonlinear model predictive control (MPC) is effective for generating diverse robotic behaviors but faces limitations with integrating multi-modal sensing and optimizing complex, real-time behaviors.

In his talk, Ludovic Righetti from NYU will explore unifying learning and numerical optimal control to address these challenges, emphasizing the integration of machine learning with online optimization and discussing the societal impacts of robotics research.

Organized by:

baiosphere

Bavarian Academy of Science and Humanities

Helmholtz Munich

LMU Munich

TUM

AI-HUB
LMU

ELLIS Munich Unit

Konrad Zuse School of Excellence in Reliable AI

MCML

Munich Data Science Institute
TUM

Munich Institute of Robotics and Machine Intelligence
TUM

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