模拟双层交互中的人际感知:在现实世界中实现机器人辅助的社会调解
Hifza Javed1, Weinan Wang1,2, Affan Bin Usman1
1Honda Research Institute USA, Inc., San Jose, CA, United States.
Frontiers in robotics and AI
|December 13, 2024
概括
社交机器人可以通过理解关系影响来改善互动. 这种新的多式模式预测了视听线索的关系影响,使机器人能够有效地调解对话.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 社交中介机器人旨在增强人类互动,但需要理解复杂的人际关系动态.
- 当前的影响识别模型往往侧重于个人的情绪,忽视了关键的人际关系影响.
研究的目的:
- 开发一种多模式,多视角的计算模型,用于识别人类互动中的关系影响.
- 让社会机器人能够更好地理解和调解实时的社会动态.
主要方法:
- 利用来自不受控制的设置对话的多式联络数据集.
- 提取的视听特征以捕捉情感行为,表明关系性情感.
- 开发了一种考虑相互作用者的观点的模型,以预测关系影响.
主要成果:
- 拟议的模型证明了预测关系影响的早期能力.
- 该模型可能能够在负面互动中进行主动干预,并加强中立/积极对话.
- 该模型显示了社会调解在现实世界中适用的优势.
结论:
- 关系影响的计算模型对于机器人有效的社会调解至关重要.
- 这项工作推动了人工智能系统的发展,这些系统可以理解和导航复杂的人类社会动态.
- 需要进一步的研究来解决人类互动的复杂性和主观性,以便在现实世界中部署.
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