Practical Verification of Neural Network Enabled State Estimation System for Robotics



Huang, Wei, Zhou, Yifan ORCID: 0000-0002-1477-5777, Sun, Youcheng, Sharp, James, Maskell, Simon ORCID: 0000-0003-1917-2913 and Huang, Xiaowei ORCID: 0000-0001-6267-0366
(2020) Practical Verification of Neural Network Enabled State Estimation System for Robotics. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020-10-24 - 2021-1-24.

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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.

Item Type: Conference or Workshop Item (Unspecified)
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 17 May 2021 07:39
Last Modified: 15 Mar 2024 11:31
DOI: 10.1109/IROS45743.2020.9340720
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3122682