Contact-based tasks such as assembly and grinding often require the information on contact states. This letter therefore proposes a recognition method based on the Mel Frequency Cepstrum Coefficient (MFCC) of force signals. It demonstrates that the combination of MFCCs and time delayed neural networks is effective for learning features of contact events. This method is able to recognize instantaneous responses that do not generate repetitive waveforms. As a result, the recognition rate of the click response during a pen cap closing task increased from 75% to 96% following the proposed method. It is confirmed that this method is applicable not only to the data obtained by the robot's highly reproducible motions but also to the data whose parameters are scattered due to human interference.

中文翻译：

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基于MFCC的力信号接触特征识别


基于接触的任务（例如装配和磨削）通常需要有关接触状态的信息。因此，这封信提出了一种基于力信号梅尔频率倒谱系数（MFCC）的识别方法。它表明 MFCC 和延时神经网络的结合对于学习接触事件的特征是有效的。该方法能够识别不产生重复波形的瞬时响应。结果，按照所提出的方法，关闭笔帽任务期间点击响应的识别率从 75% 提高到 96%。经证实，该方法不仅适用于机器人高度可重复运动获得的数据，而且适用于因人为干扰而导致参数分散的数据。