94 citations · 391 across the 65 of their papers we have counts for
17 papers · 1 filter
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…
Single Trajectory Nonparametric Learning of Nonlinear Dynamics
Ingvar Ziemann, Henrik Sandberg, Nikolai Matni
Given a single trajectory of a dynamical system, we analyze the performance of the nonparametric least squares estimator (LSE). More precisely, we give nonasymptotic expected …
Linear Variational State-Space Filtering
Daniel Pfrommer, Nikolai Matni
We introduce Variational State-Space Filters (VSSF), a new method for unsupervised learning, identification, and filtering of latent Markov state space models from raw pixels. We p…