Generation of Meaningful Robot Expressions with Active Learning

We propose a mechanism to communicate emotions to humans by using head, torso and arm movements of a humanoid robot, without exploiting its facial features. To this end, we build a library of pre-programmed robot movements and we ask people to attribute emotional scores to these initial movements. The answers are then used to fine-tune motion parameters with an active learning approach.