Data Driven Non-Verbal Behavior Generation for Humanoid Robots

Social robots need non-verbal behavior to make an interaction pleasant and efficient. Most of the models for generating non-verbal behavior are rule-based and hence can produce a limited set of motions and are tuned to a particular scenario. In contrast, data-driven systems are flexible and easily adjustable. Hence we aim to learn a data-driven model for generating non-verbal behavior (in a form of a 3D motion sequence) for humanoid robots. Our approach is based on a popular and powerful deep generative model: Variation Autoencoder (VAE). Input for our model will be multi-modal and we will iteratively increase its complexity: first, it will only use the speech signal, then also the text transcription and finally - the non-verbal behavior of the conversation partner. We will evaluate our system on the virtual avatars as well as on two humanoid robots with different embodiments: NAO and Furhat. Our model will be easily adapted to a novel domain: this can be done by providing application specific training data.