Intention and Engagement Recognition for Personalized Human-Robot Interaction, an integrated and Deep Learning approach

Abstract

The quality of the interaction between two individuals depends upon not only exchange (i.e. understanding partner's intention and reacting to it), but also on how personalized is the interaction. In this work, we have set out to accomplish these objectives for Human Robot Interaction. For this, we have developed a distributed and multimodal data acquisition and interaction manager architecture aiming to enable personalized Human-Robot Interactions. In the proposed approach, high-level perceptual capabilities (i.e. recognizing human activity and engagement) are performed by an Autoencoder, which is a Deep Learning and Unsupervised Learning method. This Autoencoder module is integrated with a facial recognition and a dialog manager (speech recognition and speech generation) to enable personalized interaction. We discuss the advantages of Autoencoders over Supervised Learning methods, and how our proposed architecture can be used to increase the duration of interaction with a robot during unscripted scenarios. Experimental validations are also performed in real Human-Robot interactions using a humanoid robot.