  As robots increasingly enter human-centered environments, they must not only
be able to navigate safely around humans, but also adhere to complex social
norms. Humans often rely on non-verbal communication through gestures and
facial expressions when navigating around other people, especially in densely
occupied spaces. Consequently, robots also need to be able to interpret
gestures as part of solving social navigation tasks. To this end, we present
Gesture2Path, a novel social navigation approach that combines image-based
imitation learning with model-predictive control. Gestures are interpreted
based on a neural network that operates on streams of images, while we use a
state-of-the-art model predictive control algorithm to solve point-to-point
navigation tasks. We deploy our method on real robots and showcase the
effectiveness of our approach for the four gestures-navigation scenarios:
left/right, follow me, and make a circle. Our experiments indicate that our
method is able to successfully interpret complex human gestures and to use them
as a signal to generate socially compliant trajectories for navigation tasks.
We validated our method based on in-situ ratings of participants interacting
with the robots.
