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Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction by Yang, Shuo; Pappas, George J.; Mangharam, Rahul; Lindemann, Lars is a scholarly article available to read on EtoBox.

What is Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction about?

We consider perception-based control using state estimates that are obtained from high-dimensional sensor measurements via learning-enabled perception maps. However, these perception maps are not perfect and result in state estimation errors that can lead to unsafe system behavior. Stochastic sensor noise can make matters worse and result in estimation errors that follow unknown distributions. We propose a perception-based control framework that i) quantifies estimation uncertainty of perception maps, and ii) integrates these uncertainty representations into the control design. To do so, we use conformal prediction to compute valid state estimation regions, which are sets that contain the unknown state with high probability. We then devise a sampled-data controller for continuous-time systems based on the notion of measurement robust control barrier functions. Our controller uses idea from self-triggered control and enables us to avoid using stochastic calculus. Our framework is agnostic to the choice of the perception map, independent of the noise distribution, and to the best of our knowledge the first to provide probabilistic safety guarantees in such a setting. We demonstrate t

Author

Yang, Shuo; Pappas, George J.; Mangharam, Rahul; Lindemann, Lars

Published

2023

Language

EN