  Robots are increasingly being used in a variety of applications, from
manufacturing and healthcare to education and customer service. However, the
mobility, power, and price points of these robots often dictate that they do
not have sufficient computing power on board to run modern algorithms for
personalization in human-robot interaction at desired rates. This can limit the
effectiveness of the interaction and limit the potential applications for these
robots. 5G connectivity provides a solution to this problem by offering high
data rates, bandwidth, and low latency that can facilitate robotics services.
Additionally, the widespread availability of cloud computing has made it easy
to access almost unlimited computing power at a low cost. Edge computing, which
involves placing compute resources closer to the action, can offer even lower
latency than cloud computing. In this paper, we explore the potential of
combining 5G, edge, and cloud computing to provide improved personalization in
human-robot interaction. We design, develop, and demonstrate a new framework,
entitled NetROS-5G, to show how the performance gained by utilizing these
technologies can overcome network latency and significantly enhance
personalization in robotics. Our results show that the integration of 5G
network slicing, edge computing, and cloud computing can collectively offer a
cost-efficient and superior level of personalization in a modern human-robot
interaction scenario.
