  Robot navigation in human semi-static and crowded environments can lead to
the freezing problem, where the robot can not move due to the presence of
humans standing on its path and no other path is available. Classical
approaches of robot navigation do not provide a solution for this problem. In
such situations, the robot could interact with the humans in order to clear its
path instead of considering them as unanimated obstacles. In this work, we
propose a robot behavior for a wheeled humanoid robot that complains with
social norms for clearing its path when the robot is frozen due to the presence
of humans. The behavior consists of two modules: 1) A detection module, which
make use of the Yolo v3 algorithm trained to detect human hands and human arms.
2) A gesture module, which make use of a policy trained in simulation using the
Proximal Policy Optimization algorithm. Orchestration of the two models is done
using the ROS framework.
