基于大幅面分布式柔性压力传感器的机器狗自然触觉交互
Enable Natural Tactile Interaction for Robot Dog based on Large-format Distributed Flexible Pressure Sensors
摘要：
触觉是人机交互的重要通道，而如何准确识别人的触觉并做出相应的反应是机器人面临的一个挑战。在本文中，我们设计并实现了一套大幅面分布式柔性压力传感器的机器狗，使自然的人与机器人的触觉交互。通过启发式研究，我们整理出了人类与真狗互动时常用的81种触觉手势和44种狗的反应。提出了一种基于ResNet的手势分类算法，对这81个人体手势进行识别，分类准确率达到98.7%。此外，提出了一种基于Transformer的动作预测算法，用于从人类手势中预测狗的动作，达到了0.87的1克BLEU分数。最后，我们在一个自由的人-机器人-狗交互游戏研究中比较了触觉交互和语音交互。结果表明，触觉交互在缓解用户焦虑、激发用户兴奋和提高机器狗的可接受性方面具有更显著的作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
