A MultiModal Social Robot Toward Personalized Emotion Interaction
Robotics
2021-10-12 v1 Artificial Intelligence
Abstract
Human emotions are expressed through multiple modalities, including verbal and non-verbal information. Moreover, the affective states of human users can be the indicator for the level of engagement and successful interaction, suitable for the robot to use as a rewarding factor to optimize robotic behaviors through interaction. This study demonstrates a multimodal human-robot interaction (HRI) framework with reinforcement learning to enhance the robotic interaction policy and personalize emotional interaction for a human user. The goal is to apply this framework in social scenarios that can let the robots generate a more natural and engaging HRI framework.
Cite
@article{arxiv.2110.05186,
title = {A MultiModal Social Robot Toward Personalized Emotion Interaction},
author = {Baijun Xie and Chung Hyuk Park},
journal= {arXiv preprint arXiv:2110.05186},
year = {2021}
}
Comments
Presented at AI-HRI symposium as part of AAAI-FSS 2021 (arXiv:2109.10836)