Degree of Contribution

Lead

Document Type

Conference Proceeding

Keywords

Robotics, Reinforcement learning, Software Engineering

Disciplines

Artificial Intelligence and Robotics | Computer Engineering | Robotics | Software Engineering

Abstract

It is important to be able to establish formal performance bounds for autonomous systems. However, formal verification techniques require a model of the environment in which the system operates; a challenge for autonomous systems, especially those expected to operate over longer timescales. This paper describes work in progress to automate the monitor and repair of ROS-based autonomous robot software written for an a-priori partially known and possibly incorrect environment model. A taint analysis method is used to automatically extract the data-flow sequence from input topic to publish topic, and instrument that code. A unique reinforcement learning approximation of MDP utility is calculated, an empirical and non-invasive characterization of the inherent objectives of the software designers. By comparing design (a-priori) utility with deploy (deployed system) utility, we show, using a small but real ROS example, that it’s possible to monitor a performance criterion and relate violations of the criterion to parts of the software. The software is then patched using automated software repair techniques and evaluated against the original off-line utility.

Publication Title

2020 IEEE Workshop on Assured Autonomous Systems

Article Number

1076

Publication Date

Spring 5-2020

Publisher

IEEE

Language

English

Peer Reviewed

1

Version

Published

Subjects

Robotics, Software Engineering

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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