Deep Learning meets Control Theory: Control Research at NNAISENSE
03 June 2020 - 03 June 2020
NNAISENSE is a startup originating from IDSIA. The Swiss AI lab has a long lasting track record of ground-breaking results in artificial intelligence (AI). From perception to reinforcement learning, the company follows IDSIA’s steps in the search for super-human performance to take AI technology into manufacturing and control systems. While AI approaches control problems from an information theoretical and statistical perspective, control theory studies the closed-loop behaviour of the physical world with a strong focus on safety, hard constraints and theoretical guarantees. Control approaches can be very robust but they can seldom be conservative due to their assumptions. This is believed to be less of a problem for Neural Networks (NN) and AI, where non-conservative results can be achieved but it is generally harder to have formal results. NN performance depends mainly on the quality and the amount of data and no unified framework exist for the analysis of stability and robustness of NN-driven control and Reinforcement Learning (RL). For this reason, while deep learning is becoming the industry standard for perception, its use in control is mostly limited to simulated or non-critical tasks. Combining the fields of control and AI has the potential for retaining the best of both Worlds. This talk will introduce NNAISENSE’s most significant publications in this emerging field, with a special focus on the latest one: “Neural Lyapunov Model Predictive Control”.

The speaker

Marco Gallieri is a Senior Researcher at NNAISENSE, in Lugano. Since September 2017, he has been forming and leading a team of experienced researchers working at the intersection of dynamics & control and AI. While being industry-focused, they authored several publications in top AI venues. His research interests include the study of safety of deep and recurrent neural networks as well as their use in control systems for safety-critical applications and safe RL.
Before joining NNAISENSE, he spent nearly four years with the McLaren group, where he developed a model based Li-Ion battery management system for the F1 power unit and a prototype for next generation F1 driver-in-the-loop simulator. He then worked as a data scientist in the R&D consulting branch of the group.
He received a PhD in Engineering from Sidney Sussex College, the University of Cambridge, in 2014. His PhD Thesis was on LASSO-MPC and is published by Springer.  In 2009 he received an MSc in automation engineering from the Universita’ Politecnica delle Marche, in Italy. He wrote his MSc thesis during a visiting term at the National University of Ireland, Maynooth.  In 2010 he was a Marie Curie early stage researcher at the Instituto Superior Tecnico in Lisbon working on non-linear formation control algorithms for autonomous underwater vehicles.