服务机器人的触觉手势识别

为了实现机器人在人机交互过程中的触觉感知，提出了一种用于服务机器人的触觉手势识别方法。首先，将电子皮肤安装在服务机器人上，通过采集15位被试者的10种手势动作信号，构建了情感手势数据集。然后，使用时空分离卷积神经网络，对被试者触摸服务机器人时做出的触摸手势进行分类。结果表明，被试内手势识别率为90.25%，跨被试手势识别率为83.44%。通过调节模型中的时空通道调节因子，在几乎不降低识别率的同时，可以大幅减少模型参数量。基于电子皮肤的触觉手势识别实验，初步认为使用时空分离卷积神经网络能够以较高的准确率和较低的计算代价实现对人的触觉手势识别，这为服务机器人通过电子皮肤与人实现情感交互提供了可能。

Touch Gesture Recognition for Service Robots

In order to realize tactile perception of robots in the process of human-robot interaction, a touch gesture recognition method for service robots is proposed. Firstly, the electronic skin is installed on the service robot, and an affective gesture dataset is built by collecting 10 kinds of touch gesture signals from 15 subjects. Then, a factorized spatio-temporal convolutional neural network ((2+1)D CNN) is used to classify the gestures applied to the service robot. The results show that the gesture recognition accuracies within-subject and across-subject are 90.25% and 83.44%, respectively. By changing the adjustment factors of spatio-temporal channels in the model, the model parameters can be greatly reduced while the recognition accuracy decreases slightly. Based on the touch gesture recognition experiment using electronic skin, it is preliminarily believed that the use of (2+1)D CNN can achieve human touch gesture recognition with higher accuracy and lower computational cost, which can realize the emotional interaction between service robots and humans through electronic skin.