Abstract
Recognizing surrounding objects is an important skill for the autonomy of robots performing in daily-life. Nowadays robots are equipped with sophisticated sensors imitating the human sense of touch. This allows the recognition of an object based on information ensuing from robot-object physical interaction. Such information can include the object texture, compliance and material. In this thesis, we exploit haptic data to perform haptic recognition of daily life objects using machine learning techniques. The main challenge faced in our work is the difficulty of collecting a fair amount of haptic training data for all daily-life objects. This is due to the continuously growing number of objects and to the effort and time needed by the robot to physically interact with each object for data collection. We solve this problem by developing a haptic recognition framework capable of performing Zero-shot, One-shot and Multi-shot Learning. We also extend our framework by integrating vision to enhance the robot’s recognition performance, whenever such sense is available.