  While imitation learning for vision based autonomous mobile robot navigation
has recently received a great deal of attention in the research community,
existing approaches typically require state action demonstrations that were
gathered using the deployment platform. However, what if one cannot easily
outfit their platform to record these demonstration signals or worse yet the
demonstrator does not have access to the platform at all? Is imitation learning
for vision based autonomous navigation even possible in such scenarios? In this
work, we hypothesize that the answer is yes and that recent ideas from the
Imitation from Observation (IfO) literature can be brought to bear such that a
robot can learn to navigate using only ego centric video collected by a
demonstrator, even in the presence of viewpoint mismatch. To this end, we
introduce a new algorithm, Visual Observation only Imitation Learning for
Autonomous navigation (VOILA), that can successfully learn navigation policies
from a single video demonstration collected from a physically different agent.
We evaluate VOILA in the photorealistic AirSim simulator and show that VOILA
not only successfully imitates the expert, but that it also learns navigation
policies that can generalize to novel environments. Further, we demonstrate the
effectiveness of VOILA in a real world setting by showing that it allows a
wheeled Jackal robot to successfully imitate a human walking in an environment
using a video recorded using a mobile phone camera.
