  Human-robot interaction (HRI) has become a crucial enabler in houses and
industries for facilitating operational flexibility. When it comes to mobile
collaborative robots, this flexibility can be further increased due to the
autonomous mobility and navigation capacity of the robotic agents, expanding
their workspace and consequently, the personalizable assistance they can
provide to the human operators. This however requires that the robot is capable
of detecting and identifying the human counterpart in all stages of the
collaborative task, and in particular while following a human in crowded
workplaces. To respond to this need, we developed a unified perception and
navigation framework, which enables the robot to identify and follow a target
person using a combination of visual Re-Identification (Re-ID), hand gestures
detection, and collision-free navigation. The Re-ID module can autonomously
learn the features of a target person and use the acquired knowledge to
visually re-identify the target. The navigation stack is used to follow the
target avoiding obstacles and other individuals in the environment. Experiments
are conducted with few subjects in a laboratory setting where some unknown
dynamic obstacles are introduced.
