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It is important to be able to establish formal performance bounds forautonomous systems. However, formal verification techniques require a model ofthe environment in which the system operates; a challenge for autonomoussystems, especially those expected to operate over longer timescales. Thispaper describes work in progress to automate the monitor and repair ofROS-based autonomous robot software written for an a-priori partially known andpossibly incorrect environment model. A taint analysis method is used toautomatically extract the data-flow sequence from input topic to publish topic,and instrument that code. A unique reinforcement learning approximation of MDPutility is calculated, an empirical and non-invasive characterization of theinherent objectives of the software designers. By comparing off-line (a-priori)utility with on-line (deployed system) utility, we show, using a small but realROS example, that it's possible to monitor a performance criterion and relateviolations of the criterion to parts of the software. The software is thenpatched using automated software repair techniques and evaluated against theoriginal off-line utility.

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