  Human gaze is known to be a strong indicator of underlying human intentions
and goals during manipulation tasks. This work studies gaze patterns of human
teachers demonstrating tasks to robots and proposes ways in which such patterns
can be used to enhance robot learning. Using both kinesthetic teaching and
video demonstrations, we identify novel intention-revealing gaze behaviors
during teaching. These prove to be informative in a variety of problems ranging
from reference frame inference to segmentation of multi-step tasks. Based on
our findings, we propose two proof-of-concept algorithms which show that gaze
data can enhance subtask classification for a multi-step task up to 6% and
reward inference and policy learning for a single-step task up to 67%. Our
findings provide a foundation for a model of natural human gaze in robot
learning from demonstration settings and present open problems for utilizing
human gaze to enhance robot learning.
