Predicting Arm Movements A Multi-Variate LSTM Based Approach for Human-Robot Hand Clapping Games

Abstract

Predicting arm movements is a key issue in physical human robots interactions. It allows robots to prepare for action and meet human requirements and needs on time. Different to human action recognition, the prediction of human movements relies on few samples, namely the first ones. In this paper, we explore the use of LSTM (Long Short Term Memory) networks in deriving the final position and the time of the human hand when performing a high five game with robots. For such a context, the synchrony of human and robot movements should be achieved at early stages of the human to meet the constraints of both real-time robot control and the realism of the robot movement. The results we obtained are very encouraging and opening new questions as well. Our solution predicts acceptable final position and contact time regardless the morphology of people and their positioning.