论文标题
ICUB：使用人类奖励学习情感表达
iCub: Learning Emotion Expressions using Human Reward
论文作者
论文摘要
本研究的目的是使用奖励成型机制学习人造药物的情绪表达表示。该方法从Tamer框架中汲取灵感，用于训练多层感知器（MLP），以学习在人类机器人互动场景中对ICUB机器人表达不同的情绪。机器人使用卷积神经网络（CNN）和自组织图（SOM）的组合来识别情绪，然后学习使用MLP来表达相同的情绪。目的是教机器人对用户对情感的看法充分反应，并学习如何表达不同的情绪。
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.
