  Over the last years, social robots have been deployed in public environments
making evident the need of human-aware navigation capabilities. In this regard,
the robotics community have made efforts to include proxemics or social
conventions within the navigation approaches. Nevertheless, few works have
tackled the problem of labelling humans as an interactive agent when blocking
the robot motion trajectory. Current state of the art navigation planners will
either propose an alternative path or freeze the motion until the path is free.
We present the first prototype of a framework designed to enhance social
competency of robots while navigating in indoor environments. The
implementation is done using Navigation and Object Detection open-source
software. Specifically, the Robot Operating System (ROS) navigation stack, and
OpenCV with Caffe deep learning models and MobileNet Single Shot Detector
(SSD), respectively.
