训练有素的人形机器人可以像人类一样进行交叉模式的社会关注和冲突解决
Di Fu1,2,3, Fares Abawi3, Hugo Carneiro3
1CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
International journal of social robotics
|June 26, 2023
概括
机器人现在可以通过解决冲突的视听信息来更好地理解社会线索,模仿人类的注意力. 这项研究通过使机器人能够有效处理复杂的社会信号来改善人机交互.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 认知科学 认知科学
- 人与计算机的交互
背景情况:
- 有效的人机交互需要机器人在现实环境中处理多个社会线索.
- 对于机器人社会认知来说,传感模式之间的信息不一致性是一个重大挑战.
- 现有的系统往往难以整合和解决相互矛盾的社会信号.
研究的目的:
- 开发一种神经机器人方法,以跨模式解决社会关注中的冲突.
- 为了使机器人能够在复杂的场景中表现出类似人类的社会注意力反应.
- 研究视听一致性对人类和机器人的社会注意力的影响.
主要方法:
- 在模拟圆桌会议上与37名参与者进行了一项行为实验.
- 机身眼睛的目光 (中央) 和声音位置 (外围) 之间的操纵空间一致性.
- 在社交线索上训练了一种突出预测模型,以检测,预测和选择性地关注iCub平台上的机器人应用程序的视听信息.
主要成果:
- 人类参与者在一致的视听条件下表现更好,而非一致的条件下表现更好.
- 动态的目光转移从中央的化身有效地触发了跨模式的社会注意力反应.
- 配备受训练模型的机器人复制了类似人类的注意力反应,尽管整体性能低于人类.
结论:
- 交叉模式的冲突解决对于机器人来说至关重要,以实现类似人类的社会关注.
- 开发的突出性预测模型显示了使机器人能够处理和响应社会线索的前景.
- 这项研究通过为更复杂的人机交互提供框架,推动了社会机器人领域的发展.
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