Multiplayer Games for Learning Multirobot Coordination Algorithms
This work addresses the challenge of programming large groups of robots for complex coordination in a distributed manner, but it appears incremental as it focuses on platform design without reported results.
The paper tackles the problem of learning distributed multirobot coordination strategies by designing a networked gaming platform to study human group behavior in collaborative tasks, with the result being a method to mimic robot capabilities and investigate coordination algorithms.
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.