| Summary: | The social interaction is one of the necessary skills for social robots to better integrate into human society. However, current social robots interact mainly through audio and visual means with little reliance on haptic interaction. There still exist many obstacles for social robots to interact through touch: 1) the complex manufacturing process of the tactile sensor array is the main obstacle to lowering the cost of production; 2) the haptic interaction mode is complex and diverse. There are no social robot interaction standards and data sets for tactile interactive behavior in the public domain. In view of this, our research looks into the following aspects of tactile perception system: 1) Development of low-cost tactile sensor array, including sensor principle, simulation, manufacture, front-end electronics, examination, then applied to the social robot’s whole body; 2) Establishment of the tactile interactive model and an event-triggered perception model in a social interactive application for the social robot, then design preprocessing and classification algorithm. In this research, we use k-nearest neighbors, tree, support vector machine and other classification algorithms to classify touch behaviors into six different classes. In particular, the cosine k-nearest neighbors and quadratic support vector machine achieve an overall mean accuracy rate of more than 68%, with an individual accuracy rate of more than 80%. In short, our research provides new directions in achieving low-cost intelligent touch interaction for social robots in a real environment. The low-cost tactile sensor array solution and interactive models are expected to be applied to social robots on a large scale.
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