94 citations · 391 across the 65 of their papers we have counts for
9 papers · 1 filter
A Tutorial on the Non-Asymptotic Theory of System Identification
Ingvar Ziemann, Anastasios Tsiamis, Bruce Lee +3
This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification. We emphasize tools we deem particul…
Safety Filter Design for Neural Network Systems via Convex Optimization
Shaoru Chen, Kong Yao Chee, Nikolai Matni +2
With the increase in data availability, it has been widely demonstrated that neural networks (NN) can capture complex system dynamics precisely in a data-driven manner. However, th…
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data
Thomas T. C. K. Zhang, Leonardo F. Toso, James Anderson +1
A powerful concept behind much of the recent progress in machine learning is the extraction of common features across data from heterogeneous sources or tasks. Intuitively, using a…
Learning Complex Motion Plans using Neural ODEs with Safety and Stability Guarantees
Farhad Nawaz, Tianyu Li, Nikolai Matni +1
We propose a Dynamical System (DS) approach to learn complex, possibly periodic motion plans from kinesthetic demonstrations using Neural Ordinary Differential Equations (NODE). To…
A Data-Driven Approach to Synthesizing Dynamics-Aware Trajectories for Underactuated Robotic Systems
Anusha Srikanthan, Fengjun Yang, Igor Spasojevic +3
We consider joint trajectory generation and tracking control for under-actuated robotic systems. A common solution is to use a layered control architecture, where the top layer use…
Performance-Robustness Tradeoffs in Adversarially Robust Control and Estimation
Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani +1
While methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastica…
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek +2
A common pipeline in learning-based control is to iteratively estimate a model of system dynamics, and apply a trajectory optimization algorithm - e.g.~ - on the lea…
The noise level in linear regression with dependent data
Ingvar Ziemann, Stephen Tu, George J. Pappas +1
We derive upper bounds for random design linear regression with dependent (-mixing) data absent any realizability assumptions. In contrast to the strictly realizable martingale…
The Fundamental Limitations of Learning Linear-Quadratic Regulators
Bruce D. Lee, Ingvar Ziemann, Anastasios Tsiamis +2
We present a local minimax lower bound on the excess cost of designing a linear-quadratic controller from offline data. The bound is valid for any offline exploration policy that c…