A Force Recognition System for Distinguishing Click Responses of Various Objects
A Force Recognition System for Distinguishing Click Responses of Various Objects
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DOI：
10.1109/iros51168.2021.9636174
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Koyo Sato;S. Sakaino;T. Tsuji
中科院分区：
文献类型：
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作者：
Koyo Sato;S. Sakaino;T. Tsuji
考虑到力响应的某些特征在不同物体之间具有共性的特点，本研究提出了一种确定机器人任务完成的技术。特别是，本文重点讨论点击响应，因为它的模式在许多对象中都是常见的，尽管响应的大小因对象而异。提出了一种基于力敏感值的Mel倒谱系数与时延神经网络相结合的响应度判别器。基于该判别器，研究了如何提高对未训练对象的泛化性能，以检测不同对象的一般点击响应。实验结果表明，在训练数据中加入几种具有相近时间常数和不同点击响应幅度的对象可以提高性能。
This study proposes a technique for determining completion of robotic tasks considering a characteristics that some features on force responses are common among various objects. In particular, this paper focuses on the click response because its pattern is common to many objects, although the magnitude of the response differs depending on the object. A discriminator for detecting the click response based on the force sensor values, which combines mel-frequency cepstral coefficient and time-delay neural network is introduced. Based on this discriminator, how to improve the generalization performance to untrained objects is investigated to detect click responses in general for various objects. The experimental results show that the performance can be improved by including several kinds of objects with close time constants and different amplitudes of click responses in the training data.