Search Results for author: Bruce D. Lee

Found 8 papers, 1 papers with code

Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies

1 code implementation27 Mar 2024 Bo Wu, Bruce D. Lee, Kostas Daniilidis, Bernadette Bucher, Nikolai Matni

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.

Imitation Learning

Nonasymptotic Regret Analysis of Adaptive Linear Quadratic Control with Model Misspecification

no code implementations29 Dec 2023 Bruce D. Lee, Anders Rantzer, Nikolai Matni

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

no code implementations25 May 2023 Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni

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

no code implementations27 Mar 2023 Bruce D. Lee, Ingvar Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni

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

no code implementations1 Dec 2022 Thomas T. Zhang, Katie Kang, Bruce D. Lee, Claire Tomlin, Sergey Levine, Stephen Tu, Nikolai Matni

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.

Imitation Learning Representation Learning

Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control

no code implementations21 Mar 2022 Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni

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

no code implementations17 Nov 2021 Thomas T. C. K. Zhang, Bruce D. Lee, Hamed Hassani, Nikolai Matni

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.

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