  In the field of human-robot interaction, teaching learning agents from human
demonstrations via supervised learning has been widely studied and successfully
applied to multiple domains such as self-driving cars and robot manipulation.
However, the majority of the work on learning from human demonstrations
utilizes only behavioral information from the demonstrator, i.e. what actions
were taken, and ignores other useful information. In particular, eye gaze
information can give valuable insight towards where the demonstrator is
allocating their visual attention, and leveraging such information has the
potential to improve agent performance. Previous approaches have only studied
the utilization of attention in simple, synchronous environments, limiting
their applicability to real-world domains. This work proposes a novel imitation
learning architecture to learn concurrently from human action demonstration and
eye tracking data to solve tasks where human gaze information provides
important context. The proposed method is applied to a visual navigation task,
in which an unmanned quadrotor is trained to search for and navigate to a
target vehicle in a real-world, photorealistic simulated environment. When
compared to a baseline imitation learning architecture, results show that the
proposed gaze augmented imitation learning model is able to learn policies that
achieve significantly higher task completion rates, with more efficient paths,
while simultaneously learning to predict human visual attention. This research
aims to highlight the importance of multimodal learning of visual attention
information from additional human input modalities and encourages the community
to adopt them when training agents from human demonstrations to perform
visuomotor tasks.
