HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES

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Authors

Keywords:

OpenPose; LSTM; Interactive Intention Prediction.

Abstract

In this research, we propose a method of human robot interactive intention prediction. The proposed algorithm makes use of a OpenPose library and a Long-short term memory deep learning neural network. The neural network observes the human posture in a time series, then predicts the human interactive intention. We train the deep neural network using dataset generated by us. The experimental results show that, our proposed method is able to predict the human robot interactive intention, providing 92% the accuracy on the testing set.

References

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Published

10-05-2021

How to Cite

Thang. “HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES”. Journal of Military Science and Technology, no. 72A, May 2021, pp. 1-12, https://en.jmst.info/index.php/jmst/article/view/40.

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Section

Research Articles