Multi-agent systems are playing an increasing role in our society. However, for the agents to cooperate effectively they must be able to effectively coordinate their behaviors. Researchers often use examples of cooperation from nature as a starting point in designing multi-agent systems. A particularly interesting case is lions and hyenas. In the wild, groups of hyenas are often observed to cooperate in driving lions away from a kill in order to claim it for themselves. Because lions are much larger and can easily injure or kill a single hyena, this is a difficult, high risk/high reward behavior requiring complex cooperation by the hyenas. We have been using evolutionary computation to allow virtual hyenas to evolve behaviors to safely drive lions away from a kill. Our results suggested that some forms of limited communication significantly increase the ability of the hyenas to learn to coordinate the behaviors. To move this research from the virtual to the physical have developed a general robot design based on Commodity-Off-The-Shelf (COTS) principles. This design allows relatively inexpensive, computationally powerful, and featureful robots.