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Enable Natural Tactile Interaction for Robot Dog based on Large-format Distributed Flexible Pressure Sensors

2023-03-14 02:35:04
Lishuang Zhan, Yancheng Cao, Qitai Chen, Haole Guo, Jiasi Gao, Yiyue Luo, Shihui Guo, Guyue Zhou, Jiangtao Gong

Abstract

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.

Abstract (translated)

触摸是人类-机器人互动的重要渠道,但对于机器人准确识别人类触摸并作出适当的反应来说,仍然是一个挑战。在本文中,我们设计和实现了一组大型分布式柔性压力传感器,安装在机器人狗上,以实现自然人类-机器人触摸互动。通过启发性研究,我们 sorted 81个常用的触摸手势,其中44个是狗的反应。我们提出了基于 ResNet 的手势分类算法,以识别这些 81 个人类手势,分类准确率达到 98.7%。此外,我们提出了基于 Transformer 的行动预测算法,以预测狗的行动并从人类手势中预测狗的动作,得到 1 个单词的 BLEU 得分 0.87。最后,我们在自由人类-机器人-狗互动玩耍研究中比较了触摸互动和语音互动。结果表明,触摸互动在减轻用户焦虑、刺激用户兴奋并改善机器人狗可接受性方面发挥着更重要的作用。

URL

https://arxiv.org/abs/2303.07595

PDF

https://arxiv.org/pdf/2303.07595.pdf


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