Search Results for author:
Found 15 papers, 4 papers with code
Safe Non-Stochastic Control of Control-Affine Systems: An Online Convex Optimization Approach
We study how to safely control nonlinear control-affine systems that are corrupted with bounded non-stochastic noise, i. e., noise that is unknown a priori and that is not necessarily governed by a stochastic model.
Leveraging Untrustworthy Commands for Multi-Robot Coordination in Unpredictable Environments: A Bandit Submodular Maximization Approach
MetaBSG leverages a meta-algorithm to learn whether the robots should follow the commands or a recently developed submodular coordination algorithm, Bandit Sequential Greedy (BSG) [1], which has performance guarantees even in unpredictable and partially-observable environments.
Safe Non-Stochastic Control of Linear Dynamical Systems
We study the problem of \textit{safe control of linear dynamical systems corrupted with non-stochastic noise}, and provide an algorithm that guarantees (i) zero constraint violation of convex time-varying constraints, and (ii) bounded dynamic regret, \ie bounded suboptimality against an optimal clairvoyant controller that knows the future noise a priori.
Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments
We are motivated by the future of autonomy that involves multiple robots coordinating actions in dynamic, unstructured, and partially observable environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring.
Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control
In this paper, we enable projection-free online learning within the framework of Online Convex Optimization with Memory (OCO-M) -- OCO-M captures how the history of decisions affects the current outcome by allowing the online learning loss functions to depend on both current and past decisions.
Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi-Robot Coordination
We are motivated by the future of autonomy that involves multiple robots coordinating in dynamic, unstructured, and adversarial environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring.
Safe Control of Partially-Observed Linear Time-Varying Systems with Minimal Worst-Case Dynamic Regret
We present safe control of partially-observed linear time-varying systems in the presence of unknown and unpredictable process and measurement noise.
Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making
All robots can thus afford our algorithm, irrespective of their resources.
Outlier-Robust Estimation: Hardness, Minimally Tuned Algorithms, and Applications
We extend ADAPT and GNC to the case where the user does not have prior knowledge of the inlier-noise statistics (or the statistics may vary over time) and is unable to guess a reasonable threshold to separate inliers from outliers (as the one commonly used in RANSAC).
Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning
Since, DRM overestimates the number of attacks in each clique, in this paper we also introduce an Improved Distributed Robust Maximization (IDRM) algorithm.
Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection
In this paper, we enable the simultaneous use of non-minimal solvers and robust estimation by providing a general-purpose approach for robust global estimation, which can be applied to any problem where a non-minimal solver is available for the outlier-free case.
Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees
First, we show that even a simple linear instance of outlier rejection is inapproximable: in the worst-case one cannot design a quasi-polynomial time algorithm that computes an approximate solution efficiently.
Resilient Non-Submodular Maximization over Matroid Constraints
The objective of this paper is to focus on resilient matroid-constrained problems arising in control and sensing but in the presence of sensor and actuator failures.
Resilient Active Information Gathering with Mobile Robots
In this paper, we provide the first algorithm, enabling the following capabilities: minimal communication, i. e., the algorithm is executed by the robots based only on minimal communication between them; system-wide resiliency, i. e., the algorithm is valid for any number of denial-of-service attacks and failures; and provable approximation performance, i. e., the algorithm ensures for all monotone (and not necessarily submodular) objective functions a solution that is finitely close to the optimal.
Resilient Monotone Sequential Maximization
In this paper, we provide the first scalable algorithm, that achieves the following characteristics: system-wide resiliency, i. e., the algorithm is valid for any number of denial-of-service attacks, deletions, or failures; adaptiveness, i. e., at each time step, the algorithm selects system elements based on the history of inflicted attacks, deletions, or failures; and provable approximation performance, i. e., the algorithm guarantees for monotone objective functions a solution close to the optimal.
