Robots in a swarm are programmed with individual behaviors but then interactions with the environment and other robots produce more complex,emergent swarm behaviors.One discriminating feature of the emergent behavior is the local distri-bution of robots in any given region.In this work,we show how local observations of the robot distribution can be correlated to the environment being explored and hence the location of open-ings or obstructions can be inferred.The correlation is achieved here with a simple,single-layer neural network that generates physically intuitive weights and provides a degree of robustness by allowing for variation in the environment and number of ro-bots in the swarm.The robots are simulated assuming random motion with no communication,a minimalist model in robot sophistication,to explore the viability of cooperative sensing.We culminate our work with a demonstration of how the local distri-bution of robots in an unknown,office-like environment can be used to locate unobstructed exits.