  The imitation learning research community has recently made significant
progress towards the goal of enabling artificial agents to imitate behaviors
from video demonstrations alone. However, current state-of-the-art approaches
developed for this problem exhibit high sample complexity due, in part, to the
high-dimensional nature of video observations. Towards addressing this issue,
we introduce here a new algorithm called Visual Generative Adversarial
Imitation from Observation using a State Observer VGAIfO-SO. At its core,
VGAIfO-SO seeks to address sample inefficiency using a novel, self-supervised
state observer, which provides estimates of lower-dimensional proprioceptive
state representations from high-dimensional images. We show experimentally in
several continuous control environments that VGAIfO-SO is more sample efficient
than other IfO algorithms at learning from video-only demonstrations and can
sometimes even achieve performance close to the Generative Adversarial
Imitation from Observation (GAIfO) algorithm that has privileged access to the
demonstrator's proprioceptive state information.
