Abstract
This paper proposes a novel approach to recognizing dynamic hand gestures facilitating seamless interaction between humans and robots. Here, each robot manipulator task is assigned a specific gesture. There may be several such tasks, hence, several gestures. These gestures may be prone to several dynamic variations. All such variations for different gestures shown to the robot are accurately recognized in real-time using the proposed unsupervised model based on the Gaussian Mixture model. The accuracy during training and real-time testing prove the efficacy of this methodology.
Abstract (translated)
本文提出了一种新颖的手势识别方法，旨在促进人类与机器人之间无缝交互。该方法为每个机器人操作任务分配一个特定手势。可能有许多这样的任务，因此也会有多个手势。这些手势可能会出现多种动态变化。使用基于高斯混合模型的无监督模型，可以准确地实时识别展示给机器人的不同手势的所有此类变化。在训练和实时测试期间的准确性证明了该方法的有效性。
URL
https://arxiv.org/abs/2601.12918
