凝视和环境环境导向深度神经网络和顺序决策融合以掌握意图识别
概括
这项研究引入了一种新的框架,用于识别使用眼睛凝视数据的抓取意图,改进对移动障碍者进行辅助机器人控制. GIRSDF框架通过将视线与环境背景和时间数据相结合来提高准确性.
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
- 神经科学是一个神经科学.
背景情况:
- 掌握意图识别对于协助机器人帮助行动能力有限的人来说至关重要.
- 眼睛凝视是识别意图的一个有希望的模式,但现有的方法缺乏环境背景和时间数据集成.
- 这限制了当前基于视线的辅助系统的性能.
研究的目的:
- 开发一个改进的基于目光的框架,用于对象检测和掌握意图识别.
- 通过将视线数据与环境背景和时间信息相结合,解决现有方法的局限性.
- 为了加强对手臂和手部残疾用户的辅助机器人的控制.
主要方法:
- 提出了一个新的基于凝视的掌握意图识别和顺序决策融合框架 (GIRSDF).
- 该框架包括凝视注意力地图生成,用于意图识别的凝视-YOLO模型和顺序决策模型 (HMM,LSTM,GRU).
- 创建了一个新的数据集,Invisible,使用来自健康个体和半患者的数据进行验证.
主要成果:
- 与以往基于凝视的方法相比,GIRSDF框架在基于试验和基于实验对象的实验中表现出优异的表现.
- 该框架的运行效率约为22Hz,可实现实时掌握意图识别.
- 拟议的方法有效地将视线数据与环境背景和时间信息相结合.
结论:
- 该GIRSDF框架显著改善了辅助机器人的基于目光的掌握意图识别.
- 该研究强调了整合环境背景和时间信息以提高性能的重要性.
- 预计这项工作将刺激进一步的研究,以目光为基础的意图识别人机交互.
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