1 citations · 1 across the 15 of their papers we have counts for
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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…
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…
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…
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…
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…