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Lars Lindemann, Assistant Professor in Thomas Lord Department of Computer Science at the University of California, is a guest of Michelle Chong, Assistant Professor at the Dynamics and Control group of the department of Mechanical Engineering, ºÚÁϸ£ÀûÍø.

Title  |  Safe Control of Learning-Enabled Autonomous Systems using Conformal Prediction

Learning-enabled autonomous systems promise to enable many future technologies such as autonomous driving, intelligent transportation, and robotics. Accelerated by algorithmic and computational advances in machine learning and AI, there has been tremendous success in the design of learning-enabled autonomous systems. However, these exciting developments are accompanied by new fundamental challenges that arise regarding the safety and reliability of these increasingly complex control systems in which sophisticated algorithms interact with unknown dynamic environments. Imperfect learning and unknowns in the environment require control techniques to rigorously account for such uncertainties. I advocate for the use of conformal prediction (CP) — a statistical tool for uncertainty quantification — due to its simplicity, generality, and efficiency as opposed to existing optimization techniques that are either conservative or subject to scalability issues. I first provide an accessible introduction to CP for the non-expert. My goal is then to show how we can use CP to design probabilistically safe motion planning algorithms in dynamic environments. Specifically, we will design a model predictive controller in conjunction with (i) learning-enabled trajectory predictors to obtain predictions of the environment, and (ii) conformal prediction regions quantifying uncertainty of these predictions. We will also discuss how to deal with distribution shifts that arise when the deployed learning-enabled system deviates, e.g., due to a sim2real gap. While existing approaches quantify uncertainty heuristically, we quantify uncertainty in a distribution-free manner with probabilistic safety guarantees. Finally, we provide an extension that enables the consideration of high-level formal system specifications via mixed integer linear programing.

Program (location: Neuron 0.262)
10.15 - 10.30   Welcome with coffee
10.30 - 11.15   Lecture 
11.15 - 11.30   Q&A