  Recently, a number of learning-based models have been proposed for
multi-robot navigation. However, these models lack memory and only rely on the
current observations of the robot to plan their actions. They are unable to
leverage past observations to plan better paths, especially in complex
environments. In this work, we propose a fully differentiable and decentralized
memory-enabled architecture for multi-robot navigation and mapping called
D2M2N. D2M2N maintains a compact representation of the environment to remember
past observations and uses Value Iteration Network for complex navigation. We
conduct extensive experiments to show that D2M2N significantly outperforms the
state-of-the-art model in complex mapping and navigation task.

Robotics