Learning Empathy-Driven Emotion Expressions using Affective Modulations

Abstract

Human-Robot Interaction (HRI) studies, particularly the ones designed around social robots, use emotions as important building blocks for interaction design. In order to provide a natural interaction experience, these social robots need to recognise the emotions expressed by the users across various modalities of communication and use them to estimate an internal affective model of the interaction. These internal emotions act as motivation for learning to respond to the user in different situations, using the physical capabilities of the robot. This paper proposes a deep hybrid neural model for multi-modal affect recognition, analysis and behaviour modelling in social robots. The model uses growing self-organising network models to encode intrinsic affective states for the robot. These intrinsic states are used to train a reinforcement learning model to learn facial expression representations on the Neuro-Inspired Companion (NICO) robot, enabling the robot to express empathy towards the users.