Learning Complex Robotic Behaviors with Optimal Control

Ludovic Righetti 

Tandon School of Engineering of New York University

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 computation, 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 generation of complex behaviors through a mixture of machine learning and online optimization. Since the algorithms we design are intended for real applications that could change how we organize our societies, I will end the presentation with a broader discussion on the impacts of robotics research on society and the role engineers ought to play.

Ludovic Righetti is an Associate Professor in the Electrical and Computer Engineering Department and in the Mechanical and Aerospace Engineering Department at the Tandon School of Engineering of New York University. He holds an Engineering Diploma in Computer Science and a Doctorate in Science from the Ecole Polytechnique Fédérale de Lausanne. He was previously a postdoctoral fellow at the University of Southern California and a group leader at the Max-Planck Institute for Intelligent Systems. His work has received several awards including the 2010 Georges Giralt PhD Award, the 2011 IROS Best Paper Award, the 2016 IEEE RAS Early Career Award and the 2016 Heinz Maier-Leibnitz Prize from the German Research Foundation. His research focuses on the planning, control and learning of movements for autonomous robots, with a special emphasis on legged locomotion and manipulation. He is also interested in the broader societal impacts of robotics and AI and regularly works with international organizations on the topic, especially on issues related to peace and security.