Practical Verification of Neural Network Enabled State Estimation System for Robotics

Wei Huang, Yifan Zhou, Youcheng Sun, James Sharp, Simon Maskell, Xiaowei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We study for the first time the verification problem on learning-enabled state estimation systems for robotics, which use Bayes filter for localisation, and use deep neural network to process sensory input into observations for the Bayes filter. Specifically, we are interested in a robustness property of the systems: given a certain ability to an adversary for it to attack the neural network without being noticed, whether or not the state estimation system is able to function with only minor loss of localisation precision? For verification purposes, we reduce the state estimation systems to a novel class of labelled transition systems with payoffs and partial order relations, and formally express the robustness property as a constrained optimisation objective. Based on this, practical verification algorithms are developed. As a major case study, we work with a real-world dynamic tracking system that uses a Kalman filter (a special case of the Bayes filter) to localise and track a ground vehicle. Its perception system, based on convolutional neural networks, processes a high-resolution Wide Area Motion Imagery (WAMI) data stream. Experimental results show that our algorithms can not only verify the robustness of the WAMI tracking system but also provide useful counterexamples.
Original languageEnglish
Title of host publicationInternational Conference on Intelligent Robots and Systems (IROS)
Pages7336-7343
Number of pages8
DOIs
Publication statusPublished - 24 Oct 2020
Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Las Vegas, NV, USA
Duration: 24 Oct 202024 Jan 2021

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Period24/10/2024/01/21

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