Deploying a Deep Learning Agent for HRI with Potential "end-users" at Multiple Sheltered Housing Sites

Abstract

With the global population aging at an alarming rate, the need to find alternative ways to deliver quality assistance is becoming a pressing concern for health and care systems. To promptly provide companion-like assistance, robots need to gain social intelligence in an autonomous way, without relying on human operators. The work described in this paper aims to develop a deep learning agent that, by means of convolutional neural network architecture in the decision making loop, could understand when and how, to interact with one, or more people, gathered in a room. This was done by training a robot to assess the level of user engagement at the initiation of the interaction, so that the robot could detect the person most willing to start interacting. The robot's performance as a deep learning agent was tested through an experiment with potential ''end-users'', following an iterative process, over four days. The deep learning agent was able to take the right decision 59% of the times by the end of the experiment, from an initial success rate of 44% on the first day, proving the potential of such technologies in this application field.