iCub: Learning Emotion Expressions using Human Reward
University of Hamburg（汉堡大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
