一种由情景信息和神经调制机制驱动的机器人社会行为的同步控制策略
Renkai Liu1, Xiaorui Liu2, Hang Su3
1School of Automation, Qingdao University, No. 308 Ningxia Road, Qingdao, Shandong 266000, China; Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, Jiangsu 210000, China.
ISA transactions
|March 29, 2025
概括
这项研究介绍了一种协调的机器人行为模型,用于与多个人互动的社交机器人. 该模型使机器人能够表现出类似人类的动作和社交眼神,增强其作为伴侣的作用.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与机器人的交互
- 人工智能的人工智能
背景情况:
- 社交机器人需要复杂的行为控制才能在多个个体环境中与人类有效互动.
- 当前的机器人往往缺乏自然互动所需的微妙的社会协议和人形行为.
- 人类的社会动力学涉及到复杂的身体运动,头部定向和眼睛的凝视,这些都很难在机器人中复制.
研究的目的:
- 开发和验证一个协调的机器人行为模型,用于在动态,多个人的环境中控制社会机器人.
- 使机器人能够表现出人形行为,包括同步的身体运动,头部旋转和眼睛运动.
- 实施一个集成社会空间理论和神经调制的控制策略,以实现现实的社会互动.
主要方法:
- 构建了一个协调的机器人行为模型,结合了身体运动/定向,头部旋转和眼球运动.
- 开发了一个同步控制策略,利用社会空间理论和神经调制机制.
- 收集了RGB-D摄像头流和声场数据,以告知机器人的运动和社会凝视行为.
- 一个最佳的控制算法,最大限度地减少神经传输噪声,被用于眼睛-头部协调的凝视行为.
主要成果:
- 拟议的模型和控制策略在小邦机器人平台上成功验证.
- 实验观察表明,机器人能够在动态的多个个体场景中实现人形反应.
- 机器人有效地执行了基于实时环境和社会数据的协调运动和社会凝视行为.
结论:
- 开发的协调机器人行为模型和控制策略对于使社会机器人能够进行人型反应是有效的.
- 社会空间理论和神经调制的整合为复杂的社会环境中的机器人行为控制提供了有希望的方法.
- 进一步的验证证实了这些策略在增强人机器人社会伙伴关系方面的潜力.
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