Search Results for author:
Found 8 papers, 1 papers with code
Active Learning for Control-Oriented Identification of Nonlinear Systems
Model-based reinforcement learning is an effective approach for controlling an unknown system.
Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies
Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold great promise for enabling general-purpose robots; however, reliable generalization to new environment conditions remains a major challenge.
Nonasymptotic Regret Analysis of Adaptive Linear Quadratic Control with Model Misspecification
Toward concretely understanding the benefit of pre-training for adaptive control, we study the adaptive linear quadratic control problem in the setting where the learner has prior knowledge of a collection of basis matrices for the dynamics.
Performance-Robustness Tradeoffs in Adversarially Robust Control and Estimation
In these special cases, we demonstrate that the severity of the tradeoff depends in an interpretable manner upon system-theoretic properties such as the spectrum of the controllability gramian, the spectrum of the observability gramian, and the stability of the system.
The Fundamental Limitations of Learning Linear-Quadratic Regulators
We present a local minimax lower bound on the excess cost of designing a linear-quadratic controller from offline data.
Multi-Task Imitation Learning for Linear Dynamical Systems
In particular, we consider a setting where learning is split into two phases: (a) a pre-training step where a shared $k$-dimensional representation is learned from $H$ source policies, and (b) a target policy fine-tuning step where the learned representation is used to parameterize the policy class.
Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control
Though this fundamental tradeoff between nominal performance and robustness is known to exist, it is not well-characterized in quantitative terms.
Adversarial Tradeoffs in Robust State Estimation
We provide an algorithm to find this perturbation given data realizations, and develop upper and lower bounds on the adversarial state estimation error in terms of the standard (non-adversarial) estimation error and the spectral properties of the resulting observer.
