Generation of Gestures During Presentation for Humanoid Robots

For presentation purposes, gestures play an exceptionally important role in improving the information transmission effect. It has been demonstrated that the body language expressing the enthusiasm and intention of the presenter affects the success of the presentation and the impression on the audience. For these reasons, presentation robots are required to perform such movements; however, manual design of these movements is a difficult task. In this research, we propose a method to model the relationship between speech prosodic information and motion using a recurrent neural network, and directly generate appropriate motions using the prosodic information. This study also proposes a method for generating motions that convey the meaning of specific words. We implement the proposed method on the “Pepper” robot to evaluate its performance.