Learning of robot-to-human object handovers

Abstract: In this thesis we propose a system for robots to learn through a semisupervised approach from observations, proper handover features for objects that can be applied onto new objects. Using recordings of handovers, features are extracted for the purpose of classifying the objects through unsupervised learning. The results from the classification are used to train a network in a supervised fashion as to properly identify handover class from images. The results of this work show that objects with similar visual features are handed over in similar way and that with a limited amount of data a model can be fitted as to properly predict handover settings for an object that has been previously encountered or not.