论文标题
使用污点分析和增强学习（TARL）来修复自动机器人软件
Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software
论文作者
论文摘要
能够为自主系统建立正式的性能范围很重要。但是，正式的验证技术需要一个系统运行的环境模型。自治系统的挑战，尤其是那些预计会在更长时间尺度上运行的系统。本文介绍了正在进行的工作，以使基于ROS的自主机器人软件的监视器和维修为A撰写的A priori部分已知且可能是错误的环境模型。污点分析方法用于自动从输入主题中提取数据流序列以发布主题和该代码的仪器。计算了MDP实用程序的独特强化学习近似，这是软件设计人员固有目标的经验和非侵入性表征。通过将离线（A-Priori）实用程序与在线（已部署系统）实用程序进行比较，我们使用一个小但真实的ROS示例表明，可以监视性能标准并将对标准的违规行为与软件的某些部分联系起来。然后，使用自动软件维修技术对该软件进行修补，并根据原始离线实用程序进行评估。
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 off-line (a-priori) utility with on-line (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.
