This review paper provides an in-depth analysis of the latest advancements in applying
Machine Learning (ML) to solve the Multi-Agent Path Finding (MAPF) problem. The MAPF …

Control barrier functions (CBFs) enable guaranteed safe multi-agent navigation in the
continuous domain. The resulting navigation performance, however, is highly sensitive to …

Graph neural networks (GNNs) are information processing architectures that model
representations from networked data and allow for decentralized implementation through …

Traditional approaches for multi-agent navigation consider the environment as a fixed
constraint, despite the obvious influence of spatial constraints on agents' performance. Yet …

This work views the multi-agent system and its surrounding environment as a co-evolving
system, where the behavior of one affects the other. The goal is to take both agent actions …

Analyzing the stability of graph neural networks (GNNs) under topological perturbations is
key to understanding their transferability and the role of each architecture component …

Robotic systems are routinely used in the logistics industry to enhance operational
efficiency, but the design of robot workspaces remains a complex and manual task, which …