iCub: Learning Emotion Expressions using Human Reward

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.

@InProceedings{CCGB16, 
 	 author =  {Churamani, Nikhil and Cruz, Francisco and Griffiths, Sascha and Barros, Pablo},  
 	 title = {iCub: Learning Emotion Expressions using Human Reward}, 
 	 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Bio-inspired Social Robot Learning in Home Scenarios},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {},
 	 year = {2016},
 	 month = {Oct},
 	 publisher = {},
 	 doi = {}, 
 }