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
Recommended Citation
Lyons, Damian and Zahra, Saba, "Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software" (2020). Faculty Publications. 67.
https://research.library.fordham.edu/frcv_facultypubs/67
Version
Published
Subjects
Robotics, Software Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Robotics Commons, Software Engineering Commons
