This paper presents a novel class of self-organizing sensing agents that form a swarm and learn the static spatial process of interest through noisy measurements from neighbors for various global goals. The spatial phenomenon of interest is modeled by a Gaussian process. Each sensing agent maintains its own prediction of the Gaussian process based on measurements from neighbors. A set of biologically inspired navigation strategies are derived by exploiting the predictive posterior statistics. A unified way to prescribe a global goal for the group of agents so that a high-level behavior builds on a set of low-level simple behavior modules. As a result, collective mobility of agents emerges from a specified global goal. The proposed cooperatively learning control consists of motion coordination based on the recursive estimation of an unknown field of interest with measurement noise. The convergence properties of the proposed coordination algorithm for different situations and global goals are investigated by a simulation study.