activity
20112023
most citedLearning Decentralized Controllers for Robot Swarms with Graph Neural Networks

410 citations · 4.5k across the 91 of their papers we have counts for

collaborators

114 papers

math.OC2023

Navigation with shadow prices to optimize multi-commodity flow rates

Ignacio Boero, Igor Spasojevic, Mariana del Castillo +3

We propose a method for providing communication network infrastructure in autonomous multi-agent teams. In particular, we consider a set of communication agents that are placed alo…

cs.RO2023

A Scalable Multi-Robot Framework for Decentralized and Asynchronous Perception-Action-Communication Loops

Saurav Agarwal, Frederic Vatnsdal, Romina Garcia Camargo +3

We develop a decentralized Perception-Action-Communication (PAC) system for multi-robot teams that enables them to collaborate in large scale, outdoor environments. Our system nati…

cs.RO2023

Robust Localization of Aerial Vehicles via Active Control of Identical Ground Vehicles

Igor Spasojevic, Xu Liu, Ankit Prabhu +3

This paper addresses the problem of active collaborative localization in heterogeneous robot teams with unknown data association. It involves positioning a small number of identica…

cs.RO2023

Active Collaborative Localization in Heterogeneous Robot Teams

Igor Spasojevic, Xu Liu, Alejandro Ribeiro +2

Accurate and robust state estimation is critical for autonomous navigation of robot teams. This task is especially challenging for large groups of size, weight, and power (SWAP) co…

cs.LG2022

Multi-task Bias-Variance Trade-off Through Functional Constraints

Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana +1

Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is t…

cs.LG2022

Training Graph Neural Networks on Growing Stochastic Graphs

Juan Cervino, Luana Ruiz, Alejandro Ribeiro

Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. Based on matrix multiplications, convolutions incur in high computational…