预测和同步共声手势以提高人机交互使用深度学习模型
Enrique Fernández-Rodicio1, Christian Dondrup2, Javier Sevilla-Salcedo1
1Department of Systems Engineering and Automation, University Carlos III of Madrid, Av. de la Universidad, 30, 28911 Leganés, Spain.
Biomimetics (Basel, Switzerland)
|December 24, 2025
概括
这项研究介绍了一个新的系统,让机器人产生同步的语音和手势,增强人机交互. 深度学习模型有效地预测和调整非语言线索与口语,以实现更自然的沟通.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 机器人越来越多地执行需要人类互动的任务,因此需要将其视为合适的合作伙伴.
- 通过表情和手势实现动画机器人的外观,对于用户接受至关重要.
- 同步机器人语音和手势对自然沟通构成了重大挑战.
研究的目的:
- 开发一个能够预测和同步机器人手势与语音的系统.
- 为了使机器人能够产生支持口头沟通的共同语音手势.
- 通过增强非语言表达能力来改善人机交互.
主要方法:
- 使用基于深度学习的预测模型,将机器人语音标记为适当的表达式类型.
- 开发了一个基于规则的同步模块,以将预测的手势与特定的语音段对齐.
- 评估了两种不同的方法:具有条件随机场的循环神经网络和变压器模型.
主要成果:
- 开发的系统成功地预测和同步机器人语音的手势.
- 该系统展示了在实时交互约束下选择合适的共同语音手势的能力.
- 两种测试的深度学习架构都在手势预测和同步方面证明了其有效性.
结论:
- 拟议的系统通过生成同步的语音和手势来增强机器人的表达力.
- 这种进步有助于更自然,更有效的人机通信.
- 这项研究验证了深度学习的使用,用于创建具有改进社会交互能力的机器人.
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