Developing a Deep Learning Agent for HRI: Dataset Collection and Training

Abstract

The world population is ageing at a dramatic rate, raising new challenges for social and health care systems. Sometimes, assistance can simply derive from a social interaction between a robotic platform and human users. In these cases, robots cannot rely on human operators. Therefore, they need to gain social intelligence in a fully autonomous way. The focus of this paper is on the initial steps needed to implement a completely autonomous robotic agent able to adapt itself to its users. For this reason, an interactive data collection was carried out to gather a dataset from which the robot could learn how to respond to its users in different situations. From these data, a first evaluation of the performances of the deep learning agent, embodied in the robot, has been completed. The agent was able to generalize to new sets of test data. The study explored how, using modern machine learning algorithms, a robot could learn to understand if, and how, to interact with one, or more people, gathered in a room. This was done by training a robot to read the level of the engagement of the users at the initiation of the interaction.