Humanoids learn object properties from robust tactile feature descriptors via multi-modal artificial skin

This paper presents new methods for the recognition and categorization of object properties such as surface texture, weight, and compliance using a multi-modal artificial skin mounted on both arms of a humanoid. In addition, it introduces two novel feature descriptors, which are useful for providing high-level information to learning algorithms. The artificial skin has built-in 3-axis accelerometer, normal force, proximity, and temperature sensors. To explore different surface textures and weights, objects were left sliding between the NAO humanoid's arms. The caused vibration was detected by accelerometers. Surface texture and weight recognition models were learned from the extracted features of the vibration signals thanks to two learning algorithms, namely the support vector machine (SVM) and the Expectation Maximization (EM). In order to recognize objects having different compliances, SVM and EM took into account total amount of forces applied by the arms to hold the object firmly. The experimental results show that the humanoid can distinguish between different objects having different surface textures and weights with a recognition rate of 100%. Furthermore, it can categorize objects with hard and soft surfaces and classify objects having similar compliance with 100% and 70% accuracy rates respectively.