  In this paper, we introduce a new concept of incorporating factorized flow
maps as mid-level representations, for bridging the perception and the control
modules in modular learning based robotic frameworks. To investigate the
advantages of factorized flow maps and examine their interplay with the other
types of mid-level representations, we further develop a configurable
framework, along with four different environments that contain both static and
dynamic objects, for analyzing the impacts of factorized optical flow maps on
the performance of deep reinforcement learning agents. Based on this framework,
we report our experimental results on various scenarios, and offer a set of
analyses to justify our hypothesis. Finally, we validate flow factorization in
real world scenarios.
