  A defining characteristic of intelligent systems is the ability to make
action decisions based on the anticipated outcomes. Video prediction systems
have been demonstrated as a solution for predicting how the future will unfold
visually, and thus, many models have been proposed that are capable of
predicting future frames based on a history of observed frames~(and sometimes
robot actions). However, a comprehensive method for determining the fitness of
different video prediction models at guiding the selection of actions is yet to
be developed. Current metrics assess video prediction models based on human
perception of frame quality. In contrast, we argue that if these systems are to
be used to guide action, necessarily, the actions the robot performs should be
encoded in the predicted frames. In this paper, we are proposing a new metric
to compare different video prediction models based on this argument. More
specifically, we propose an action inference system and quantitatively rank
different models based on how well we can infer the robot actions from the
predicted frames. Our extensive experiments show that models with high
perceptual scores can perform poorly in the proposed action inference tests and
thus, may not be suitable options to be used in robot planning systems.
