Context and Intention aware 3D Human Body Motion Prediction using an Attention Deep Learning model in Handover Tasks

Abstract

This work explores how contextual information and human intention affect the motion prediction of humans during a handover operation with a social robot. By classifying human intention in four different classes, we developed a model able to generate a different motion for each intention class. Furthermore, the model uses a multi-headed attention architecture to add contextual information to the pipeline, such as the position of the robot end effector (REE) or the position of obstacles in the interaction scene. We generate predictions up to two and half seconds in the future given an input sequence of one second containing the previous motion of the human. The results show an improvement of the prediction accuracy, both for the full skeleton prediction and the human hand used for the delivery. The model also allows to generate different sequences with the desired human intention.