Gesture recognition for humanoid robot teleoperation

Interactive robotics is a vast and expanding research field. Interactions must be sufficiently natural, with robots having socially acceptable behavior by Humans, adaptable to user expectations. Thus allowing easy integration in our daily lives in various fields (science, industry, health, etc). Natural interaction during Human-Robot collaborative action needs suitable interaction techniques. In our paper we develop a gesture recognition system for natural and intuitive communication between Human and NAO robot. However recognizing meaningful gesture patterns from whole-body gestures is a complex task. That is why we used the Laban Movement Analysis technique to describe high level gestures for NAO tele-operation. The major contributions of the present work is: (1) an efficient preprocessing step based on view invariant Human motion, (2) a robust descriptor vector based on Laban Movement Analysis technique in order to generate compact and informative representations of Human movement, and (3) a gesture recognition system based on Hidden Markov Model method was applied to teleoperate NAO based on our proper database dedicated to the tele-operation of NAO. Our approach was evaluated with two challenging datasets, Microsoft Research Cambridge-12 (MSRC-12) and UTKinect-Action. Experimental results show that our approach outperforms the state-of-the-art methods.