iCub: Learning Emotion Expressions using Human Reward
This work addresses emotion expression for robots in interaction scenarios, but it is incremental as it builds on existing frameworks like TAMER.
The study tackled the problem of teaching a robot to express emotions in human-robot interaction by using a reward shaping mechanism, resulting in a system that learns to recognize and express emotions through neural networks.
The purpose of the present study is to learn emotion expression representations for artificial agents using reward shaping mechanisms. The approach takes inspiration from the TAMER framework for training a Multilayer Perceptron (MLP) to learn to express different emotions on the iCub robot in a human-robot interaction scenario. The robot uses a combination of a Convolutional Neural Network (CNN) and a Self-Organising Map (SOM) to recognise an emotion and then learns to express the same using the MLP. The objective is to teach a robot to respond adequately to the user's perception of emotions and learn how to express different emotions.