2020/03/30 by Nikhil Churamani, Francisco Cruz, Churamani, Nikhil +5 · 1 citation
Computer Science · Psychology · #Emotion and Mood Recognition #FOS: Computer and information sciences #Human Pose and Action Recognition #Human-Computer Interaction (cs.HC) #Robotics (cs.RO) #Social Robot Interaction and HRI
paper · pdf · doi:10.48550/arxiv.2003.13483
openalex publication_date 2020/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.