Visual intention classification by deep learning for gaze-based human-robot interaction

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Visual intention classification by deep learning for gaze-based human-robot interaction

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Shi, Lei / Copot, Cosmin / Vanlanduit, Steve

Abstract: In this work, we propose a deep learning model to classify a human’s visual intention in gaze-based Human-Robot Interaction(HRI). We consider a scenario in which a human wears a pair of eye tracking glasses and can select an object by gaze and a robotic manipulator picks up the object. A neural network is trained as a binary classifier to classify if a human is looking at an object. The network architecture is based on Fully Convolutional Net(FCN), Convolutional Block Attention Modules(CBAM) and Residual Blocks. We evaluate our model with two experiments. In one experiment we test the performance in the scenario where only a single object exists and the other one multiple objects exist. The results show that our proposed network is accurate and it can generalize well. The F1 score on the single object is 0.971 and 0.962 on multiple objects.