Gesture Recognition for Humanoid Robot Teleoperation

Abstract : 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 ...). Natural interaction during human-robot collaborative action needs suitable interaction techniques. In our paper we develop an online 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 teleoperation. 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 to generate compact and informative representations of Human movement, and (3) an online gesture recognition with Hidden Markov Model method was applied to teleoperate NAO based on our proper data base dedicated to the teleoperation 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.
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Insaf Ajili, Malik Mallem, Jean-Yves Didier. Gesture Recognition for Humanoid Robot Teleoperation. 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2017), Aug 2017, Lisbon, Portugal. pp.1115--1120, ⟨10.1109/ROMAN.2017.8172443⟩. ⟨hal-01627859⟩

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