A tactile sensor array is a crucial component for applying physical sensors to a humanoid robot. This work focused on developing a palm-size tactile sensor array (56.0 mm × 56.0 mm) to apply object recognition for the humanoid robot hand. This sensor was based on a PCB technology operating with the piezoresistive principle. A conductive polymer composites sheet was used as a sensing element and the matrix array of this sensor was 16 × 16 pixels. The sensitivity of this sensor was evaluated and the sensor was installed on the robot hand. The tactile images, with resolution enhancement using bicubic interpolation obtained from 20 classes, were used to train and test 19 different DCNNs. InceptionResNetV2 provided superior performance with 91.82% accuracy. However, using the multimodal learning method that included InceptionResNetV2 and XceptionNet, the highest recognition rate of 92.73% was achieved. Moreover, this recognition rate improved when the object exploration was applied to demonstrate.

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With the development of human-like robotic technologies, robots have been advanced to sense and recognize objects, which is one branch of artificial intelligence to mimic human reactions. Although high-performance computer technologies are used, the ability of object recognition by touch is still low due to the lack of proper sensors. Therefore, this research studied the object learning and recognition system through robot touches by developing an artificial sensory system acting as an electronic skin with tactile sensors. The Tactile Sensor is developed in this research, consisting of 15 Tactile Sensor Arrays and the palm's touchpoints. Furthermore, recognition analysis was developed on Bag of Word (BoW) and Convolution Neural Network (CNN) algorithms. With the BoW technique, using Support Vector Machine (SVM) as a classifier with Moment Analysis Descriptor (MA) provided the highest accuracy, showing more than 80.15% accuracy from five grasping of an object. With the CNN approach, InceptionNetV3 provided the highest accuracy of 98.28% from only one capture of an object.INDEX TERMS Bag of feature, bag of word, CNN, robot hand, tactile object recognition, tactile sensor, transfer learning.

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