# iCub: Learning Emotion Expressions using Human Reward

arXiv: https://arxiv.org/abs/2003.13483 · HTML: https://mlsanity.com/paper/2003.13483/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Persistent Rule-based Interactive Reinforcement Learning](https://mlsanity.com/paper/2102.02441/) (2021)
- [Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task](https://mlsanity.com/paper/2108.08911/) (2021)
- [Reinforcement Learning for UAV control with Policy and Reward Shaping](https://mlsanity.com/paper/2212.03828/) (2022)
- [Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents](https://mlsanity.com/paper/2108.03730/) (2021)

## Abstract (arXiv)

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.