Touch is an important channel for human-robot interaction, while it is challenging for robots to recognize human touch accurately and make appropriate responses. In this paper, we design and implement a set of large-format distributed flexible pressure sensors on a robot dog to enable natural human-robot tactile interaction. Through a heuristic study, we sorted out 81 tactile gestures commonly used when humans interact with real dogs and 44 dog reactions. A gesture classification algorithm based on ResNet is proposed to recognize these 81 human gestures, and the classification accuracy reaches 98.7%. In addition, an action prediction algorithm based on Transformer is proposed to predict dog actions from human gestures, reaching a 1-gram BLEU score of 0.87. Finally, we compare the tactile interaction with the voice interaction during a freedom human-robot-dog interactive playing study. The results show that tactile interaction plays a more significant role in alleviating user anxiety, stimulating user excitement and improving the acceptability of robot dogs.

翻译：触觉是人机交互的重要通道，但机器人准确识别人体触觉并做出恰当响应仍具挑战性。本文在机器狗上设计并实现了一套大面积分布式柔性压力传感器，以实现自然的人机触觉交互。通过启发式研究，我们梳理出人类与真实狗交互时常用的81种触觉手势及44种狗的响应动作。提出基于ResNet的手势分类算法识别这81种人体手势，分类准确率达98.7%。此外，还提出基于Transformer的动作预测算法，从人体手势预测狗的响应动作，其1-gram BLEU得分为0.87。最后，在自由人-机-狗交互游戏研究中对比触觉交互与语音交互的效果，结果表明触觉交互在缓解用户焦虑、激发用户兴奋感及提升机器狗可接受性方面具有更显著作用。