  This paper presents a novel methodology that allows a swarm of robots to
perform a cooperative transportation task. Our approach consists of modeling
the swarm as a {\em Gibbs Random Field} (GRF), taking advantage of this
framework's locality properties. By setting appropriate potential functions,
robots can dynamically navigate, form groups, and perform cooperative
transportation in a completely decentralized fashion. Moreover, these behaviors
emerge from the local interactions without the need for explicit communication
or coordination. To evaluate our methodology, we perform a series of
simulations and proof-of-concept experiments in different scenarios. Our
results show that the method is scalable, adaptable, and robust to failures and
changes in the environment.
