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Showing papers by "Paulo Tabuada published in 2022"


Journal ArticleDOI
01 Jan 2022
TL;DR: In this paper, the authors leverage recent results on neural network approximation, combined with classical input-to-state stability (ISS) properties, and show how to design deep neural networks for state estimation that guarantee the safety and stability of the resulting closed-loop system.
Abstract: Deep learning is currently used in the perception pipeline of autonomous systems, such as when estimating the system state from camera and LiDAR measurements. While this practice is typical, hard guarantees on the worst-case behavior of the closed-loop system are rare. In this letter, however, we leverage recent results on neural network approximation, combined with classical input-to-state stability (ISS) properties, and show how to design deep neural networks for state estimation that guarantee the safety and stability of the resulting closed-loop system.

7 citations


Journal ArticleDOI
01 Jan 2022
TL;DR: In this paper, the authors propose an approach to synthesize sampled-data counterparts to these control Lyapunov function (CLF) based controllers, specified as quadratically constrained quadratic programs (QCQPs).
Abstract: Controller design for nonlinear systems with Control Lyapunov Function (CLF) based quadratic programs has recently been successfully applied to a diverse set of difficult control tasks. These existing formulations do not address the gap between design with continuous time models and the discrete time sampled implementation of the resulting controllers, often leading to poor performance on hardware platforms. We propose an approach to close this gap by synthesizing sampled-data counterparts to these CLF-based controllers, specified as quadratically constrained quadratic programs (QCQPs). Assuming feedback linearizability and stable zero-dynamics of a system’s continuous time model, we derive practical stability guarantees for the resulting sampled-data system. We demonstrate improved performance of the proposed approach over continuous time counterparts in simulation.

6 citations


Proceedings ArticleDOI
23 May 2022
TL;DR: This paper shows 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.
Abstract: 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.

3 citations


Journal Article
TL;DR: In this paper , a Lyapunov-based proof for the stability of linear time-invariant control systems in controller canonical form when utilizing dirty derivatives in place of observers for the purpose of output feedback is provided.
Abstract: Dirty derivatives are routinely used in industrial settings, particularly in the implementation of the derivative term in PID control, and are especially appealing due to their noise-attenuation and model-free characteristics. In this paper, we provide a Lyapunov-based proof for the stability of linear time-invariant control systems in controller canonical form when utilizing dirty derivatives in place of observers for the purpose of output feedback. This is, to the best of the authors’ knowledge, the first time that stability proofs are provided for the use of dirty derivatives in lieu of derivatives of different orders. In the spirit of adaptive control, we also show how dirty derivatives can be used for output feedback control when the control gain is unknown.

1 citations


Proceedings ArticleDOI
06 Dec 2022
TL;DR: In this paper , a simple algorithm for point cloud registration, termed PASTA, was proposed, which is global and does not rely on point-to-point correspondences, which are typically absent in LiDAR data.
Abstract: 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.

1 citations


Proceedings ArticleDOI
06 Dec 2022
TL;DR: In this paper , the authors propose a decentralized learning algorithm that enables all nodes to reach consensus on the optimal model, in the absence of attacks, and approximate consensus in the presence of data poisoning attacks.
Abstract: This paper addresses the problem of decentralized learning in the presence of data poisoning attacks. In this problem, we consider a collection of nodes connected through a network, each equipped with a local function. The objective is to compute the global minimizer of the aggregated local functions, in a decentralized manner, i.e., each node can only use its local function and data exchanged with nodes it is connected to. Moreover, each node is to agree on the said minimizer despite an adversary that can arbitrarily change the local functions of a fraction of the nodes. This problem setting has applications in robust learning, where nodes in a network are collectively training a model that minimizes the empirical loss with possibly attacked local data sets. In this paper, we propose a novel decentralized learning algorithm that enables all nodes to reach consensus on the optimal model, in the absence of attacks, and approximate consensus in the presence of data poisoning attacks.

1 citations


Proceedings ArticleDOI
06 Jun 2022
TL;DR: This work develops a network synthesis scenario, which is built around a concrete perimeter surveillance application, yet it is believed captures a number of the challenges and requirements that are common to other tactical communication and computational network applications.
Abstract: We develop a network synthesis scenario, which is built around a concrete perimeter surveillance application, yet we believe captures a number of the challenges and requirements that are common to other tactical communication and computational network applications. The proposed scenario addresses the problem of binary population identification within a perimeter: our goal is to synthesize a sensing and computing network that classifies people moving within a given perimeter into one of two categories (e.g., friend or foe). We discuss several open challenges that we organize across the following clusters: sensor placement, communication network provisioning and optimization, computational task placement, dynamic re-synthesis and resilience under adversarial settings. We also briefly discuss approaches that attempt to address such challenges.

Proceedings ArticleDOI
06 Dec 2022
TL;DR: In this article , the authors propose a method to lift mixed problems to higher-dimensional ones satisfying the conditions, and prove that this new, lifted problem's solution directly gives a certifiably optimal or near-optimal solution to the original problem.
Abstract: Optimization problems with mixed continuous and discrete costs are notoriously difficult. In either extreme case, suitable structures such as convexity and submodularity guarantee that the problems can be solved exactly and efficiently. Recent results showed that the same structures can be used in the mixed case, provided certain sufficient conditions are met.In this work, we address a class of these mixed problems that do not satisfy the established conditions and propose a method to lift the problems to higher-dimensional ones satisfying the conditions. We then prove that this new, lifted problem’s solution directly gives a certifiably optimal or near-optimal solution to the original problem. For proof-of-concept, we verify our lifting approach with an example from retail price optimization with logistical start-up costs, comparing favorably against the current state-of-the art.

Journal ArticleDOI
TL;DR: A method to circumvent cyber-physical state estimation intrusion detection techniques while exfiltrating sensitive data from the network is proposed and a generalized model for encoding and decoding sensitive data within cyber- physical control loops is proposed.
Abstract: Although organizations are continuously making concerted efforts to harden their systems against network attacks by air-gapping critical systems, attackers continuously adapt and uncover covert channels to exfiltrate data from air-gapped systems. For instance, attackers have demonstrated the feasibility of exfiltrating data from a computer sitting in a Faraday cage by exfiltrating data using magnetic fields. Although a large body of work has recently emerged highlighting various physical covert channels, these attacks have mostly targeted open-loop cyber-physical systems where the covert channels exist on physical channels that are not being monitored by the victim. Network architectures such as fog computing push sensitive data to cyber-physical edge devices--whose physical side channels are typically monitored via state estimation. In this paper, we formalize covert data exfiltration that uses existing cyber-physical models and infrastructure of individual devices to exfiltrate data in a stealthy manner, i.e., we propose a method to circumvent cyber-physical state estimation intrusion detection techniques while exfiltrating sensitive data from the network. We propose a generalized model for encoding and decoding sensitive data within cyber-physical control loops. We evaluate our approach on a distributed IoT network that includes computation nodes residing on physical drones as well as on an industrial control system for the control of a robotic arm. Unlike prior works, we formalize the constraints of covert cyber-physical channel exfiltration in the presence of a defender performing state estimation.

Journal ArticleDOI
TL;DR: This paper shows 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.
Abstract: In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert. By first 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. When we have more than n+ 1 demonstrations, we discuss how to optimally choose the best n + 1 demonstrations to construct the stabilizing controller. We then extend these results to a class of systems that can be embedded into a higher-dimensional system containing a chain of integrators. The feasibility of the proposed algorithm is demonstrated by applying it on a CrazyFlie 2.0 quadrotor.