  Object transportation could be a challenging problem for a single robot due
to the oversize and/or overweight issues. A multi-robot system can take the
advantage of increased driving power and more flexible configuration to solve
such a problem. However, increased number of individuals also changed the
dynamics of the system which makes control of a multi-robot system more
complicated. Even worse, if the whole system is sitting on a centralized
decision making unit, the data flow could be easily overloaded due to the
upscaling of the system. In this research, we propose a decentralized control
scheme on a multi-robot system with each individual equipped with a deep
Q-network (DQN) controller to perform an oversized object transportation task.
DQN is a deep reinforcement learning algorithm thus does not require the
knowledge of system dynamics, instead, it enables the robots to learn
appropriate control strategies through trial-and-error style interactions
within the task environment. Since analogous controllers are distributed on the
individuals, the computational bottleneck is avoided systematically. We
demonstrate such a system in a scenario of carrying an oversized rod through a
doorway by a two-robot team. The presented multi-robot system learns abstract
features of the task and cooperative behaviors are observed. The decentralized
DQN-style controller is showing strong robustness against uncertainties. In
addition, We propose a universal metric to assess the cooperation
quantitatively.
