Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software

Authors

Damian Lyons, Fordham UniversityFollow
Saba Zahra, Fordham University

Comments

IEEE Workshop on Assured Autonomous Systems, May 2020

Disciplines

Artificial Intelligence and 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.

Recommended Citation

Lyons, Damian and Zahra, Saba, "Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software" (2020). Videos. 2.
https://research.library.fordham.edu/frcv_videos/2