  Getting robots to navigate to multiple objects autonomously is essential yet
difficult in robot applications. One of the key challenges is how to explore
environments efficiently with camera sensors only. Existing navigation methods
mainly focus on fixed cameras and few attempts have been made to navigate with
active cameras. As a result, the agent may take a very long time to perceive
the environment due to limited camera scope. In contrast, humans typically gain
a larger field of view by looking around for a better perception of the
environment. How to make robots perceive the environment as efficiently as
humans is a fundamental problem in robotics. In this paper, we consider
navigating to multiple objects more efficiently with active cameras.
Specifically, we cast moving camera to a Markov Decision Process and
reformulate the active camera problem as a reinforcement learning problem.
However, we have to address two new challenges: 1) how to learn a good camera
policy in complex environments and 2) how to coordinate it with the navigation
policy. To address these, we carefully design a reward function to encourage
the agent to explore more areas by moving camera actively. Moreover, we exploit
human experience to infer a rule-based camera action to guide the learning
process. Last, to better coordinate two kinds of policies, the camera policy
takes navigation actions into account when making camera moving decisions.
Experimental results show our camera policy consistently improves the
performance of multi-object navigation over four baselines on two datasets.
