Visual Gesture Recognition for Human Robot Interaction Using Dynamic Movement Primitives

In this paper a method to address the efficiency and robustness of dynamic hand gesture recognition for human robot interaction is proposed. By using on-board monocular camera and specialized gesture detection algorithms, the humanoid robot is able to detect gestures fast. To model the dynamics of gestures, the dynamic movement primitives (DMP) model is employed, which well characterizes both spatial and temporal evolutions of gestures. The invariance properties of the DMP model against different spatiotemporal scales also offer expected robustness to handle the variances in gestures. To cope with the diversity and noise of gestures, an efficient adaptive DMP learning method is further proposed. Since the learnt weights of the DMP compactly represent the original gestures, they serve as ideal feature vectors for building a classifier to recognize new gestures. To evaluate the proposed method, a nine-class human gestures recognition task on a real humanoid robot is performed and 98.06% accuracy is obtained. Experimental results demonstrate the effectiveness of our method.