94 citations · 391 across the 65 of their papers we have counts for
73 papers
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…
Multi-Task Imitation Learning for Linear Dynamical Systems
Thomas T. Zhang, Katie Kang, Bruce D. Lee +4
We study representation learning for efficient imitation learning over linear systems. In particular, we consider a setting where learning is split into two phases: (a) a pre-train…
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles
Kong Yao Chee, M. Ani Hsieh, Nikolai Matni
Nonlinear model predictive control (MPC) is a flexible and increasingly popular framework used to synthesize feedback control strategies that can satisfy both state and control inp…
Distributed Optimal Control of Graph Symmetric Systems via Graph Filters
Fengjun Yang, Fernando Gama, Somayeh Sojoudi +1
Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and infor…
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning
David Brandfonbrener, Stephen Tu, Avi Singh +4
We consider how to most efficiently leverage teleoperator time to collect data for learning robust image-based value functions and policies for sparse reward robotic tasks. To acco…
Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis
Shaoru Chen, Ning-Yuan Li, Victor M. Preciado +1
We present a robust model predictive control method (MPC) for discrete-time linear time-delayed systems with state and control input constraints. The system is subject to both poly…
Statistical Learning Theory for Control: A Finite Sample Perspective
Anastasios Tsiamis, Ingvar Ziemann, Nikolai Matni +1
This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification. While there has been su…
How are policy gradient methods affected by the limits of control?
Ingvar Ziemann, Anastasios Tsiamis, Henrik Sandberg +1
We study stochastic policy gradient methods from the perspective of control-theoretic limitations. Our main result is that ill-conditioned linear systems in the sense of Doyle inev…
Toward Certified Robustness Against Real-World Distribution Shifts
Haoze Wu, Teruhiro Tagomori, Alexander Robey +6
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts. To do so, we bridge the gap between hand-crafted specifications…
Learning to Control Linear Systems can be Hard
Anastasios Tsiamis, Ingvar Ziemann, Manfred Morari +2
In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regre…
TaSIL: Taylor Series Imitation Learning
Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu +1
We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control. TaSIL penalizes deviations in t…
Risk of Stochastic Systems for Temporal Logic Specifications
Lars Lindemann, Lejun Jiang, Nikolai Matni +1
The wide availability of data coupled with the computational advances in artificial intelligence and machine learning promise to enable many future technologies such as autonomous…
Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control
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…
Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis
Shaoru Chen, Victor M. Preciado, Manfred Morari +1
We propose a robust model predictive control (MPC) method for discrete-time linear systems with polytopic model uncertainty and additive disturbances. Optimizing over linear time-v…
Distributed and Localized Model Predictive Control. Part II: Theoretical Guarantees
Carmen Amo Alonso, Jing Shuang Li, Nikolai Matni +1
Engineered cyberphysical systems are growing increasingly large and complex. These systems require scalable controllers that robustly satisfy state and input constraints in the pre…
Uncertainty-driven Planner for Exploration and Navigation
Georgios Georgakis, Bernadette Bucher, Anton Arapin +3
We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constit…