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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…