We have conducted two investigations on the ability of human participants to solve challenging collective coordination tasks in a distributed fashion with limited perception and communication capabilities similar to those of a simple ground robot. In these investigations, participants were gathered in a laboratory of networked workstations and were given a series of different collective tasks with varying communication and perception capabilities. Here, we focus on our latest investigation and describe our methodology, platform design considerations, and highlight some interesting observed behaviors. These investigations are the preliminary phase in designing a formal strategy for learning human-inspired behaviors for solving complex distributed multirobot problems, such as pattern formation.