Search Results for author:
Found 55 papers, 21 papers with code
Convex Relaxations of SE(2) and SE(3) for Visual Pose Estimation
The method is a convex relaxation of the classical pose estimation problem, and is based on explicit linear matrix inequality (LMI) representations for the convex hulls of $SE(2)$ and $SE(3)$.
On the Sample Complexity of the Linear Quadratic Regulator
This paper addresses the optimal control problem known as the Linear Quadratic Regulator in the case when the dynamics are unknown.
Finite-Data Performance Guarantees for the Output-Feedback Control of an Unknown System
As the systems we control become more complex, first-principle modeling becomes either impossible or intractable, motivating the use of machine learning techniques for the control of systems with continuous action spaces.
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator
We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown linear system is controlled subject to quadratic costs.
Safely Learning to Control the Constrained Linear Quadratic Regulator
We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques.
Learning Sparse Dynamical Systems from a Single Sample Trajectory
In particular, we show that the proposed estimator can correctly identify the sparsity pattern of the system matrices with high probability, provided that the length of the sample trajectory exceeds a threshold.
A Tutorial on Concentration Bounds for System Identification
We provide a brief tutorial on the use of concentration inequalities as they apply to system identification of state-space parameters of linear time invariant systems, with a focus on the fully observed setting.
From self-tuning regulators to reinforcement learning and back again
Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world.
Robust Guarantees for Perception-Based Control
Motivated by vision-based control of autonomous vehicles, we consider the problem of controlling a known linear dynamical system for which partial state information, such as vehicle position, is extracted from complex and nonlinear data, such as a camera image.
Efficient Learning of Distributed Linear-Quadratic Controllers
In this work, we propose a robust approach to design distributed controllers for unknown-but-sparse linear and time-invariant systems.
Sample Complexity of Kalman Filtering for Unknown Systems
We show that when the system identification step produces sufficiently accurate estimates, or when the underlying true KF is sufficiently robust, that a Certainty Equivalent (CE) KF, i. e., one designed using the estimated parameters directly, enjoys provable sub-optimality guarantees.
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i. e., the confidence set for a given input contains the true label with high probability.
An Adversarial Objective for Scalable Exploration
Model-based curiosity combines active learning approaches to optimal sampling with the information gain based incentives for exploration presented in the curiosity literature.
Learning Control Barrier Functions from Expert Demonstrations
Furthermore, if the CBF parameterization is convex, then under mild assumptions, so is our learning process.
Learning Stability Certificates from Data
Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function that guarantees a desired property.
Learning Hybrid Control Barrier Functions from Data
Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data.
Data-Driven System Level Synthesis
We establish data-driven versions of the System Level Synthesis (SLS) parameterization of achievable closed-loop system responses for a linear-time-invariant system over a finite-horizon.
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems
We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions.
On the Sample Complexity of Stability Constrained Imitation Learning
We study the following question in the context of imitation learning for continuous control: how are the underlying stability properties of an expert policy reflected in the sample-complexity of an imitation learning task?
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?
The difficulty of optimal control problems has classically been characterized in terms of system properties such as minimum eigenvalues of controllability/observability gramians.
STL Robustness Risk over Discrete-Time Stochastic Processes
We then define the risk of a stochastic process not satisfying an STL formula robustly, referred to as the STL robustness risk.
Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control
Our proposed parameterization enjoys a local and distributed architecture, similar to previous Graph Neural Network (GNN)-based parameterizations, while further naturally allowing for joint optimization of the distributed controller and communication topology needed to implement it.
System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation
We propose a robust model predictive control (MPC) method for discrete-time linear time-invariant systems with norm-bounded additive disturbances and model uncertainty.
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.
Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations
Along with the optimization problem, we provide verifiable conditions in terms of the density of the data, smoothness of the system model and state estimator, and the size of the error bounds that guarantee validity of the obtained ROCBF.
Adversarially Robust Stability Certificates can be Sample-Efficient
Motivated by bridging the simulation to reality gap in the context of safety-critical systems, we consider learning adversarially robust stability certificates for unknown nonlinear dynamical systems.
Data-driven Distributed and Localized Model Predictive Control
By imposing locality constraints on the system response, we show that the amount of data needed for our synthesis problem is independent of the size of the global system.
Linear Variational State-Space Filtering
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.
Single Trajectory Nonparametric Learning of Nonlinear Dynamics
Given a single trajectory of a dynamical system, we analyze the performance of the nonparametric least squares estimator (LSE).
Uncertainty-driven Planner for Exploration and Navigation
We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constitute these tasks challenging.
Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis
However, it is challenging to design LTV state feedback controllers in the face of model uncertainty whose effects are difficult to bound.
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.
Learning to Control Linear Systems can be Hard
In this paper, we study the statistical difficulty of learning to control linear systems.
Risk of Stochastic Systems for Temporal Logic Specifications
For discrete-time stochastic processes, we show under which conditions the approximate STL robustness risk can even be computed exactly.
TaSIL: Taylor Series Imitation Learning
We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control.
Toward Certified Robustness Against Real-World Distribution Shifts
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts.
How are policy gradient methods affected by the limits of control?
We study stochastic policy gradient methods from the perspective of control-theoretic limitations.
Statistical Learning Theory for Control: A Finite Sample Perspective
This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification.
Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis
In the proposed method, a time-varying feedback control policy is optimized such that the robust satisfaction of all constraints for the closed-loop system is guaranteed.
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning
We find that by adjusting the data collection process we improve the quality of both the learned value functions and policies over a variety of baseline methods for data collection.
Distributed Optimal Control of Graph Symmetric Systems via Graph Filters
Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and information exchange structure, e. g., sparsity, delay, or spatial invariance.
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles
We show that the KNODE ensemble provides more accurate predictions and illustrate the efficacy and closed-loop performance of the proposed nonlinear MPC framework using two case studies.
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.
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.
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
A common pipeline in learning-based control is to iteratively estimate a model of system dynamics, and apply a trajectory optimization algorithm - e. g.~$\mathtt{iLQR}$ - on the learned model to minimize a target cost.
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.
Safety Filter Design for Neural Network Systems via Convex Optimization
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.
A Tutorial on the Non-Asymptotic Theory of System Identification
This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification.
EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields
We present EvDNeRF, a pipeline for generating event data and training an event-based dynamic NeRF, for the purpose of faithfully reconstructing eventstreams on scenes with rigid and non-rigid deformations that may be too fast to capture with a standard camera.
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.
Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems
In this paper, we argue that adapting the trajectory generation component keeping the controller fixed can improve trajectory tracking for quadrotor systems experiencing drag forces.
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
We show that whenever the topologies of $L^2$ and $\Psi_p$ are comparable on our hypothesis class $\mathscr{F}$ -- that is, $\mathscr{F}$ is a weakly sub-Gaussian class: $\|f\|_{\Psi_p} \lesssim \|f\|_{L^2}^\eta$ for some $\eta\in (0, 1]$ -- the empirical risk minimizer achieves a rate that only depends on the complexity of the class and second order statistics in its leading term.
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.
Active Learning for Control-Oriented Identification of Nonlinear Systems
Model-based reinforcement learning is an effective approach for controlling an unknown system.
