Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Formal Verification of Robustness and Resilience of Learning-Enabled
  State Estimation Systems for Robotics   This paper presents a formal verification guided approach for a principled
design and implementation of robust and resilient learning-enabled systems. We
focus on learning-enabled state estimation systems (LE-SESs), which have been
widely used in robotics applications to determine the current state (e.g.,
location, speed, direction, etc.) of a complex system. The LE-SESs are
networked systems composed of a set of connected components including Bayes
filters for localisation, and neural networks for processing sensory input. We
study LE-SESs from the perspective of formal verification, which determines the
satisfiability of a system model against the specified properties. Over
LE-SESs, we investigate two key properties - robustness and resilience - and
provide their formal definitions. To enable formal verification, we reduce the
LE-SESs to a novel class of labelled transition systems, named {PO}2-LTS in the
paper, and formally express the properties as constrained optimisation
objectives. We prove that the robustness verification is NP-complete. Based on
{PO}2-LTS and the optimisation objectives, practical verification algorithms
are developed to check the satisfiability of the properties on the LE-SESs. As
a major case study, we interrogate a real-world dynamic tracking system which
uses a single Kalman Filter (KF) - a special case of 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 properties of the WAMI tracking system but also provide representative
examples, the latter of which inspired us to take an enhanced LE-SESs design
where runtime monitors or joint-KFs are required. Experimental results confirm
the improvement of the robustness of the enhanced design.

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The categories of this paper are: math
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The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org