In this work, we present a decentralized motion planning and control architecture for the cooperative loading task using heterogeneous robotic agents operating in a cluttered workspace with static obstacles. Initially, we tackle the problem of calculating a set of feasible loading configurations via a Probabilistic Road Maps technique. Next, an optimal loading configuration is selected considering the connectivity of the space and the Euclidean distance between the robotic agents. A motion control scheme for each agent is designed and implemented in order to autonomously guide each robot to the desired loading configuration with guaranteed obstacle avoidance and convergence properties. The performance and the applicability of the proposed strategy is experimentally verified in a variety of loading scenarios using a redundant static manipulator and a mobile platform.