  In visual semantic navigation, the robot navigates to a target object with
egocentric visual observations and the class label of the target is given. It
is a meaningful task inspiring a surge of relevant research. However, most of
the existing models are only effective for single-agent navigation, and a
single agent has low efficiency and poor fault tolerance when completing more
complicated tasks. Multi-agent collaboration can improve the efficiency and has
strong application potentials. In this paper, we propose the multi-agent visual
semantic navigation, in which multiple agents collaborate with others to find
multiple target objects. It is a challenging task that requires agents to learn
reasonable collaboration strategies to perform efficient exploration under the
restrictions of communication bandwidth. We develop a hierarchical decision
framework based on semantic mapping, scene prior knowledge, and communication
mechanism to solve this task. The results of testing experiments in unseen
scenes with both known objects and unknown objects illustrate the higher
accuracy and efficiency of the proposed model compared with the single-agent
model.
