论文标题
手势识别以启动人类到机器人的移交
Gesture Recognition for Initiating Human-to-Robot Handovers
论文作者
论文摘要
人向机器人的移交对于许多人类机器人的互动场景很有用。重要的是要认识到人何时打算启动移交，以便在不打算交接时，机器人不会试图从人类那里夺取对象。我们在单个RGB图像中将移交手势识别作为二进制分类问题。实现了三个用于检测对象的神经网络模块，即人体的关键点和头部方向，以从RGB图像中提取相关特征，然后将特征向量传递到深神经网中以执行二进制分类。我们的结果表明，正确识别移交手势的精度超过90％。这些特征的抽象使我们的方法模块化，并且可以推广到不同的物体和人体类型。
Human-to-Robot handovers are useful for many Human-Robot Interaction scenarios. It is important to recognize when a human intends to initiate handovers, so that the robot does not try to take objects from humans when a handover is not intended. We pose the handover gesture recognition as a binary classification problem in a single RGB image. Three separate neural network modules for detecting the object, human body key points and head orientation, are implemented to extract relevant features from the RGB images, and then the feature vectors are passed into a deep neural net to perform binary classification. Our results show that the handover gestures are correctly identified with an accuracy of over 90%. The abstraction of the features makes our approach modular and generalizable to different objects and human body types.
