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Real-time cyber-physical systems (CPS) rely on Perception-Cognition-Actuation (PCA) pipelines to enable autonomous observation, decisionmaking, and action execution. Closed-loop PCA systems utilize feedback-driven control to iteratively adapt actions in response to real-time environmental changes whereas open-loop PCA systems execute single actions without iterative feedback. The overall performance of these systems is inherently tied to the models selected for each pipeline component. Recent advancements in neural networks, particularly for perception tasks, have substantially enhanced CPS capabilities but have introduced significant complexity into the PCA pipeline. While traditional research [1] often evaluates perception models in static, controlled settings, it fails to account for the cascading latency and accuracy trade-offs that manifest across interconnected PCA modules in dynamic, real-time applications. Additionally, the proliferation of distributed device-edge-cloud architectures [2] has expanded computational possibilities but introduced new challenges in balancing latency and accuracy with resource constraints. The holistic impact of model selection, deployment platforms, and network conditions on application performance in real-time scenarios remains under-explored. 

We revisit the problem of computing (robust) controlled invariant sets for discrete-time linear systems. Departing from previous approaches, we consider implicit, rather than explicit, representations for controlled invariant sets. Moreover, by considering such representations in the space of states and finite input sequences we obtain closed-form expressions for controlled invariant sets. An immediate advantage is the ability to handle high-dimensional systems since the closed-form expression is computed in a single step rather than iteratively. To validate the proposed method, we present thorough case studies illustrating that in safety-critical scenarios the implicit representation suffices in place of the explicit invariant set. The proposed method is complete in the absence of disturbances, and we provide a weak completeness result when disturbances are present. 

In recent years, LiDAR sensors have become pervasive in the solutions to localization tasks for autonomous systems. One key step in using LiDAR data for localization is the alignment of two LiDAR scans taken from different poses, a process called scan-matching or point cloud registration. Most existing algorithms for this problem are heuristic in nature and local, meaning they may not produce accurate results under poor initialization. Moreover, existing methods give no guarantee on the quality of their output, which can be detrimental for safety-critical tasks. In this paper, we analyze a simple algorithm for point cloud registration, termed PASTA. This algorithm is global and does not rely on point-to-point correspondences, which are typically absent in LiDAR data. Moreover, and to the best of our knowledge, we offer the first point cloud registration algorithm with provable error bounds. Finally, we illustrate the proposed algorithm and error bounds in simulation on a simple trajectory tracking task. 

In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert. By focusing on feedback linearizable systems, we show how to combine expert demonstrations into a stabilizing controller, provided that demonstrations are sufficiently long and there are at least n+1 of them, where n is the number of states of the system being controlled. The results are experimentally demonstrated on a CrazyFlie 2.0 quadrotor. 

We study the decentralized resilient state-tracking problem in which each node in a network has the objective of tracking the state of a linear dynamical system based on its local measurements and information exchanged with its neighboring nodes, despite an attack on some of the nodes. We propose a novel algorithm that solves the decentralized resilient state-tracking problem by relating it to the dynamic average consensus problem. Compared with existing solutions in the literature, our algorithm provides a solution for the most general class of decentralized resilient state-tracking problem instances.