Multiplayer Games for Learning Multirobot Coordination Algorithms

TL;DR

This paper introduces a multiplayer gaming platform designed to study human group behavior in complex coordination tasks, aiming to develop distributed multirobot coordination algorithms inspired by human strategies.

Contribution

It presents a novel networked gaming platform that simulates robot-like capabilities to investigate human coordination, facilitating the development of robust multirobot algorithms.

Findings

Platform successfully limits communication and sensing to mimic robots.

Human strategies provide insights for robot coordination algorithms.

Potential for scalable, fault-tolerant multirobot systems.

Abstract

Humans have an impressive ability to solve complex coordination problems in a fully distributed manner. This ability, if learned as a set of distributed multirobot coordination strategies, can enable programming large groups of robots to collaborate towards complex coordination objectives in a way similar to humans. Such strategies would offer robustness, adaptability, fault-tolerance, and, importantly, distributed decision-making. To that end, we have designed a networked gaming platform to investigate human group behavior, specifically in solving complex collaborative coordinated tasks. Through this platform, we are able to limit the communication, sensing, and actuation capabilities provided to the players. With the aim of learning coordination algorithms for robots in mind, we define these capabilities to mimic those of a simple ground robot.