Robots and automation systems have become a
valuable partner in several facets of human life: from learning
and teaching, to daily working, including health monitoring
and assistance. So far, these appealing robot-based applications
are restricted to conduct repetitive, yet useful, tasks due to the
reduced individual robots’ capabilities in terms of processing
and computation. This concern prevents current robots from
facing more complex applications related to understanding hu-
man beings and perceiving their subtle feelings. Such hardware
limitations have been already found in the computer science
field. In this domain, they are currently being addressed using
a new resource exploitation model coined as cloud computing,
which is targeted at enabling massive storage and computation
using smartly connected and inexpensive commodity hardware.
The purpose of this paper is to propose a cloud-based robotics
architecture to effectively develop complex tasks related to
hospitalized children assistance. More specifically, this paper
presents a multi-agent learning system that combines machine
learning and cloud computing using low-cost robots to (1)
collect and perceive children status, (2) build a human-readable
set of rules related to the child-robot relationship, and (3)
improve the children experience during their stay in the hos-
pital. Conducted preliminary experiments proof the feasibility
of this proposal and encourage practitioners to work towards
this direction.Peer ReviewedPostprint (published version
