Abbas, Houssam
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Publication Synthesizing stealthy reprogramming attacks on cardiac devices(2019-04-16) Paoletti, Nicola; Jiang, Zhihao; Islam, Ariful; Abbas, Houssam; Mangharam, Rahul; Lin, Shan; Smolka, Scott A.; Gruber, ZacharyAn Implantable Cardioverter Defibrillator (ICD) is a medical device used for the detection of potentially fatal cardiac arrhythmias and their treatment through the delivery of electrical shocks intended to restore normal heart rhythm. An ICD reprogramming attack seeks to alter the device’s parameters to induce unnecessary therapy or prevent required therapy. In this paper, we present a formal approach for the synthesis of ICD reprogramming attacks that are both effective, i.e., lead to fundamental changes in the required therapy, and stealthy, i.e., are hard to detect. We focus on the discrimination algorithm underlying Boston Scientific devices (one of the principal ICD manufacturers) and formulate the synthesis problem as one of multi-objective optimization. Our solution technique is based on an Optimization Modulo Theories encoding of the problem and allows us to derive device parameters that are optimal with respect to the effectiveness-stealthiness trade-off. Our method can be tailored to the patient’s current condition, and readily generalizes to new rhythms. To the best of our knowledge, our work is the first to derive systematic ICD reprogramming attacks designed to maximize therapy disruption while minimizing detection.Publication Temporal Logic Robustness for General Signal Classes(2019-04-15) Abbas, Houssam; Pant, Yash Vardhan; Mangharam, RahulIn multi-agent systems, robots transmit their planned trajectories to each other or to a central controller, and each receiver plans its own actions by maximizing a measure of mission satisfaction. For missions expressed in temporal logic, the robustness function plays the role of satisfaction measure. Currently, a Piece-Wise Linear (PWL) or piece-wise constant reconstruction is used at the receiver. This allows an efficient robustness computation algorithm - a.k.a. monitoring - but is not adaptive to the signal class of interest, and does not leverage the compression properties of more general representations. When communication capacity is at a premium, this is a serious bottleneck. In this paper we first show that the robustness computation is significantly affected by how the continuous-time signal is reconstructed from the received samples, which can mean the difference between a successful control and a crash. We show that monitoring general spline-based reconstructions yields a smaller robustness error, and that it can be done with the same time complexity as monitoring the simpler PWL reconstructions. Thus robustness computation can now be adapted to the signal class of interest. We further show that the monitoring error is tightly upper-bounded by the L ∞ signal reconstruction error. We present a (non-linear) L ∞ -based scheme which yields even lower monitoring error than the spline-based schemes (which have the advantage of being faster to compute), and illustrate all results on two case studies. As an application of these results, we show how time-frequency specifications can be efficiently monitored online.Publication Technical Report: Anytime Computation and Control for Autonomous Systems(2019-04-15) Pant, Yash Vardhan; Abbas, Houssam; Mohta, Kartik; Quaye, Rhudii A.; Nghiem, Truong X; Devietti, Joseph; Mangharam, RahulThe correct and timely completion of the sensing and action loop is of utmost importance in safety critical autonomous systems. A crucial part of the performance of this feedback control loop are the computation time and accuracy of the estimator which produces state estimates used by the controller. These state estimators, especially those used for localization, often use computationally expensive perception algorithms like visual object tracking. With on-board computers on autonomous robots being computationally limited, the computation time of a perception-based estimation algorithm can at times be high enough to result in poor control performance. In this work, we develop a framework for co-design of anytime estimation and robust control algorithms while taking into account computation delays and estimation inaccuracies. This is achieved by constructing a perception-based anytime estimator from an off-the-shelf perception-based estimation algorithm, and in the process we obtain a trade-off curve for its computation time versus estimation error. This information is used in the design of a robust predictive control algorithm that at run-time decides a contract for the estimator, or the mode of operation of estimator, in addition to trying to achieve its control objectives at a reduced computation energy cost. In cases where the estimation delay can result in possibly degraded control performance, we provide an optimal manner in which the controller can use this trade-off curve to reduce estimation delay at the cost of higher inaccuracy, all the while guaranteeing that control objectives are robustly satisfied. Through experiments on a hexrotor platform running a visual odometry algorithm for state estimation, we show how our method results in upto a 10% improvement in control performance while saving 5-6% in computation energy as compared to a method that does not leverage the co-design.Publication Teaching Autonomous Systems at 1/10th-scale(2020-01-01) Agnihotri, Abhijeet; O'Kelly, Matthew; Mangharam, Rahul; Abbas, HoussamTeaching autonomous systems is challenging because it is a rapidly advancing cross-disciplinary field that requires theory to be continually validated on physical platforms. For an autonomous vehicle (AV) to operate correctly, it needs to satisfy safety and performance properties that depend on the operational context and interaction with environmental agents, which can be difficult to anticipate and capture. This paper describes a senior undergraduate level course on the design, programming and racing of 1/10th-scale autonomous race cars. We explore AV safety and performance concepts at the limits of perception, planning, and control, in a highly interactive and competitive environment. The course includes an ethics-centered design philosophy, which seeks to engage the students in an analysis of ethical and socio-economic implications of autonomous systems. Our hypothesis is that 1/10th-scale autonomous vehicles sufficiently capture the scaled dynamics, sensing modalities, decision making and risks of real autonomous vehicles, but are a safe and accessible platform to teach the foundations of autonomous systems. We describe the design, deployment and feedback from two offerings of this class for college seniors and graduate students, open-source community development across 36 universities, international racing competitions, student skill enhancement and employability, and recommendations for tailoring it to various settings.Publication Fly-by-Logic: A Tool for Unmanned Aircraft System Fleet Planning using Temporal Logic(2019-05-07) Pant, Yash Vardhan; Quaye, Rhudii A.; Abbas, Houssam; Varre, Akarsh; Mangharam, RahulSafe planning for fleets of Unmaned Aircraft Systems (UAS) performing complex missions in urban environments has typically been a challenging problem. In the United States of America, the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) have been studying the regulation of the airspace when multiple such fleets of autonomous UAS share the same airspace, outlined in the Concept of Operations document (ConOps). While the focus is on the infrastructure and management of the airspace, the Unmanned Aircraft System (UAS) Traffic Management (UTM) ConOps also outline a potential airspace reservation based system for operation where operators reserve a volume of the airspace for a given time inter- val to operate in, but it makes clear that the safety (separation from other aircraft, terrain, and other hazards) is a responsibility of the drone fleet operators. In this work, we present a tool that allows an operator to plan out missions for fleets of multi-rotor UAS, performing complex time- bound missions. The tool builds upon a correct-by-construction planning method by translating missions to Signal Temporal Logic (STL). Along with a simple user interface, it also has fast and scalable mission planning abilities. We demonstrate our tool for one such mission.