A MultiModal Social Robot Toward Personalized Emotion Interaction
A MultiModal Social Robot Toward Personalized Emotion Interaction
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发表时间：
2021-10
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通讯作者：
Baijun Xie;C. Park
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作者：
Baijun Xie;C. Park
人类的情感通过多种形式表达，包括语言信息和非语言信息。此外，人类用户的情感状态可以作为参与程度和成功交互的指标，适合作为机器人通过交互来优化机器人行为的奖励因素。本研究提出了一种基于强化学习的多通道人机交互(HRI)框架，以增强机器人交互策略，并为人类用户提供个性化的情感交互。我们的目标是将这个框架应用到社交场景中，让机器人生成一个更自然和更吸引人的HRI框架。
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.