1 citations · 1 across the 15 of their papers we have counts for
21 papers
Co-designing a Layered Control Architecture
Chris Verhoek, Charis Stamouli, George J. Pappas +1
Layered control architectures (LCAs) are ubiquitous in complex engineering systems. However, in practice they are largely designed ad hoc and mostly dictated by the experience of t…
AnyRank: Reliable Anytime Policy Ranking via Sequential Testing
Apurva Badithela, David Snyder, Tenny Yin +6
Due to the high cost of hardware experiments, robot policies are predominantly evaluated by comparing their empirical performance on a limited number of hardware trials. However, f…
Physics-Informed Learning of Feedback-Linearizing Representations
Pavlos Kallinikidis, Fengjun Yang, David Snyder +4
Feedback linearization is a powerful tool in nonlinear control, but finding the linearizing coordinate transform remains challenging. Feedback linearizing transforms are governed b…
Policy Gradient over History-Dependent Policy Classes for LQR with Domain Randomization
Tesshu Fujinami, Bruce D. Lee, Anastasios Tsiamis +2
Domain Randomization (DR) has been widely used to overcome the sim-to-real gap by training a controller on a distribution of simulated environments via reinforcement learning. Whil…
Verifying performance, stability, and feasibility of inexact non-linear model predictive controllers
Rajiv Sambharya, Sribalaji C. Anand, George Pappas
We introduce a verification framework to numerically analyze inexact model predictive controllers (MPCs) in the constrained non-linear discrete-time setting. Rather than modifying…
Multitask LQG Control: Performance and Generalization Bounds
Leonardo F. Toso, Kasra Fallah, Charis Stamouli +2
We study multitask learning for stochastic and partially observed control systems, focusing on the linear quadratic Gaussian (LQG) problem. Our goal is to learn a common stabilizin…
Adversarial Robustness of Deep State Space Models for Forecasting
Sribalaji C. Anand, George J. Pappas
State-space model (SSM) for time-series forecasting have demonstrated strong empirical performance on benchmark datasets, yet their robustness under adversarial perturbations is po…
Statistical Efficiency of Single- and Multi-step Models for Forecasting and Control
Anne Somalwar, Bruce D. Lee, George J. Pappas +1
Compounding error, where small prediction mistakes accumulate over time, presents a major challenge in learning-based control. A common remedy is to train multi-step predictors dir…
Beyond Binary Success: Sample-Efficient and Statistically Rigorous Robot Policy Comparison
David Snyder, Apurva Badithela, Nikolai Matni +4
Generalist robot manipulation policies are becoming increasingly capable, but are limited in evaluation to a small number of hardware rollouts. This strong resource constraint in r…
Verification of Sequential Convex Programming for Parametric Non-convex Optimization
Rajiv Sambharya, Nikolai Matni, George Pappas
We introduce a verification framework to exactly verify the worst-case performance of sequential convex programming (SCP) algorithms for parametric non-convex optimization. The ver…
Learning Acceleration Algorithms for Fast Parametric Convex Optimization with Certified Robustness
Rajiv Sambharya, Jinho Bok, Nikolai Matni +1
We develop a machine-learning framework to learn hyperparameter sequences for accelerated first-order methods (e.g., the step size and momentum sequences in accelerated gradient de…
Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error
Anne Somalwar, Bruce D. Lee, George J. Pappas +1
Compounding error, where small prediction mistakes accumulate over time, presents a major challenge in learning-based control. For example, this issue often limits the performance…
Policy Gradient for LQR with Domain Randomization
Tesshu Fujinami, Bruce D. Lee, Nikolai Matni +1
Domain randomization (DR) enables sim-to-real transfer by training controllers on a distribution of simulated environments, with the goal of achieving robust performance in the rea…
Domain Randomization is Sample Efficient for Linear Quadratic Control
Tesshu Fujinami, Bruce D. Lee, Nikolai Matni +1
We study the sample efficiency of domain randomization and robust control for the benchmark problem of learning the linear quadratic regulator (LQR). Domain randomization, which sy…
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
Thomas T. Zhang, Bruce D. Lee, Ingvar Ziemann +2
A driving force behind the diverse applicability of modern machine learning is the ability to extract meaningful features across many sources. However, many practical domains invol…
Flying Quadrotors in Tight Formations using Learning-based Model Predictive Control
Kong Yao Chee, Pei-An Hsieh, George J. Pappas +1
Flying quadrotors in tight formations is a challenging problem. It is known that in the near-field airflow of a quadrotor, the aerodynamic effects induced by the propellers are com…
State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?
Ingvar Ziemann, Nikolai Matni, George J. Pappas
How many parameters are required for a model to execute a given task? It has been argued that large language models, pre-trained via self-supervised learning, exhibit emergent capa…
Recursively Feasible Shrinking-Horizon MPC in Dynamic Environments with Conformal Prediction Guarantees
Charis Stamouli, Lars Lindemann, George J. Pappas
In this paper, we focus on the problem of shrinking-horizon Model Predictive Control (MPC) in uncertain dynamic environments. We consider controlling a deterministic autonomous sys…
Rate-Optimal Non-Asymptotics for the Quadratic Prediction Error Method
Charis Stamouli, Ingvar Ziemann, George J. Pappas
We study the quadratic prediction error method -- i.e., nonlinear least squares -- for a class of time-varying parametric predictor models satisfying a certain identifiability cond…
Active Learning for Control-Oriented Identification of Nonlinear Systems
Bruce D. Lee, Ingvar Ziemann, George J. Pappas +1
Model-based reinforcement learning is an effective approach for controlling an unknown system. It is based on a longstanding pipeline familiar to the control community in which one…
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
Ingvar Ziemann, Stephen Tu, George J. Pappas +1
In this work, we study statistical learning with dependent (-mixing) data and square loss in a hypothesis class where is the norm $\|f\|_{Ψ_p}…