
Safe Pontryagin Differentiable Programming
We propose a Safe Pontryagin Differentiable Programming (Safe PDP) metho...
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Encrypted Distributed Lasso for Sparse Data Predictive Control
The least squares problem with L1regularized regressors, called Lasso, ...
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STL Robustness Risk over DiscreteTime Stochastic Processes
We present a framework to interpret signal temporal logic (STL) formulas...
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Linear Systems can be Hard to Learn
In this paper, we investigate when system identification is statisticall...
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Technical Report: Scalable Active Information Acquisition for MultiRobot Systems
This paper proposes a novel highly scalable nonmyopic planning algorith...
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Resilient Active Information Acquisition with Teams of Robots
Emerging applications of collaborative autonomy, such as MultiTarget Tr...
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ModelBased Domain Generalization
We consider the problem of domain generalization, in which a predictor i...
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Achieving Linear Convergence in Federated Learning under Objective and Systems Heterogeneity
We consider a standard federated learning architecture where a group of ...
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NonMonotone EnergyAware Information Gathering for Heterogeneous Robot Teams
This paper considers the problem of planning trajectories for a team of ...
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Is the brain macroscopically linear? A system identification of resting state dynamics
A central challenge in the computational modeling of neural dynamics is ...
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SensorBased Temporal Logic Planning in Uncertain Semantic Maps
This paper addresses a multirobot mission planning problem in uncertain...
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Reactive Temporal Logic Planning for Multiple Robots in Unknown Occupancy Grid Maps
This paper proposes a new reactive temporal logic planning algorithm for...
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Scalable Reinforcement Learning Policies for MultiAgent Control
This paper develops a stochastic MultiAgent Reinforcement Learning (MAR...
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Technical Report: Reactive Planning for Mobile Manipulation Tasks in Unexplored Semantic Environments
Complex manipulation tasks, such as rearrangement planning of numerous o...
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Private Weighted Sum Aggregation
As large amounts of data are circulated both from users to a cloud serve...
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Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes
This paper focuses on the controller synthesis for unknown, nonlinear sy...
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Datadriven control on encrypted data
We provide an efficient and private solution to the problem of encryptio...
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Zerothorder Deterministic Policy Gradient
Deterministic Policy Gradient (DPG) removes a level of randomness from s...
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ModelBased Robust Deep Learning
While deep learning has resulted in major breakthroughs in many applicat...
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Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example
The ability to store and manipulate information is a hallmark of computa...
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Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees
The fragility of deep neural networks to adversariallychosen inputs has...
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ReachSDP: Reachability Analysis of ClosedLoop Systems with Neural Network Controllers via Semidefinite Programming
There has been an increasing interest in using neural networks in closed...
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Technical Report: Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback
This paper presents a reactive planning system that enriches the topolog...
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Sample Complexity of Kalman Filtering for Unknown Systems
In this paper, we consider the task of designing a Kalman Filter (KF) fo...
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RiskAware MMSE Estimation
Despite the simplicity and intuitive interpretation of Minimum Mean Squa...
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ModelFree Learning of Optimal Ergodic Policies in Wireless Systems
Learning optimal resource allocation policies in wireless systems can be...
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Statistical Learning for Analysis of Networked Control Systems over Unknown Channels
Recent control trends are increasingly relying on communication networks...
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Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller
This paper describes a verification case study on an autonomous racing c...
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Learning Qnetwork for Active Information Acquisition
In this paper, we propose a novel Reinforcement Learning approach for so...
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Probabilistic Verification and Reachability Analysis of Neural Networks via Semidefinite Programming
Quantifying the robustness of neural networks or verifying their safety ...
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Distributed AttackRobust Submodular Maximization for MultiRobot Planning
We aim to guard swarmrobotics applications against denialofservice (D...
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Optimal Algorithms for Submodular Maximization with Distributed Constraints
We consider a class of discrete optimization problems that aim to maximi...
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Robust and Adaptive Sequential Submodular Optimization
Emerging applications of control, estimation, and machine learning, rang...
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Source Seeking in Unknown Environments with Convex Obstacles
Navigation tasks often cannot be defined in terms of a target, either be...
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Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees
Reinforcement Learning (RL) has emerged as an efficient method of choice...
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Secure Multiparty Computation for Cloudbased Control
In this chapter, we will explore the cloudoutsourced privacypreserving...
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Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Tight estimation of the Lipschitz constant for deep neural networks (DNN...
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Finite Sample Analysis of Stochastic System Identification
In this paper, we analyze the finite sample complexity of stochastic sys...
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Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming
Analyzing the robustness of neural networks against normbounded uncerta...
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LatencyReliability Tradeoffs for State Estimation
The emerging interest in lowlatency highreliability applications, such...
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Resilient Active Target Tracking with Multiple Robots
The problem of target tracking with multiple robots consists of actively...
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Cloudbased Quadratic Optimization with Partially Homomorphic Encryption
The development of largescale distributed control systems has led to th...
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Resilient NonSubmodular Maximization over Matroid Constraints
Applications in control, robotics, and optimization motivate the design ...
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Cloudbased MPC with Encrypted Data
This paper explores the privacy of cloud outsourced Model Predictive Con...
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Resilient Active Information Gathering with Mobile Robots
Applications in robotics, such as multirobot target tracking, involve t...
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Resilient Monotone Sequential Maximization
Applications in machine learning, optimization, and control require the ...
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LQG Control and Sensing Codesign
LinearQuadraticGaussian (LQG) control is concerned with the design of ...
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Control and Sensing Codesign
LinearQuadraticGaussian (LQG) control is concerned with the design of ...
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Dense 3D Mapping with Spatial Correlation via Gaussian Filtering
Constructing an occupancy representation of the environment is a fundame...
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On the Limited Communication Analysis and Design for Decentralized Estimation
This paper pertains to the analysis and design of decentralized estimati...
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George J. Pappas
verfied profile
George J. Pappas is the UPS Foundation Professor and Chair of the Department of Electrical and Systems Engineering at the University of Pennsylvania. He also holds a secondary appointment in the Departments of Computer and Information Sciences, and Mechanical Engineering and Applied Mechanics. He is member of the GRASP Lab and the PRECISE Center.