HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES

Authors

  • Do Nam Thang Academy of Military Science and Technology

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

. M. Shiomi, F. Zanlungo, K. Hayashi, and T. Kanda, "Towards a socially acceptable collision avoidance for a mobile robot navigating among pedestrians using a pedestrian model," International Journal of Social Robotics, vol. 6, no. 3, pp. 443-455, 2014.

. X.-T. Truong and T. D. Ngo, "Toward socially aware robot navigation in dynamic and crowded environments: A proactive social motion model," IEEE Transactions on Automation Science and Engineering, vol. 14, no. 4, pp. 1743-1760, 2017.

. Y. F. Chen, M. Everett, M. Liu, and J. P. How, "Socially aware motion planning with deep reinforcement learning," in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017: IEEE, pp. 1343-1350.

. X. T. Truong and T. D. Ngo, "Social interactive intention prediction and categorization," in ICRA 2019 Workshop on MoRobAE-Mobile Robot Assistants for the Elderly, Montreal Canada, May 20-24, 2019.

. Y. Li and S. S. Ge, "Human–robot collaboration based on motion intention estimation," IEEE/ASME Transactions on Mechatronics, vol. 19, no. 3, pp. 1007-1014, 2013.

. J. S. Park, C. Park, and D. Manocha, "I-planner: Intention-aware motion planning using learning-based human motion prediction," The International Journal of Robotics Research, vol. 38, no. 1, pp. 23-39, 2019.

. R. Kelley, A. Tavakkoli, C. King, M. Nicolescu, M. Nicolescu, and G. Bebis, "Understanding human intentions via hidden markov models in autonomous mobile robots," in Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, 2008, pp. 367-374.

. T. Bandyopadhyay, K. S. Won, E. Frazzoli, D. Hsu, W. S. Lee, and D. Rus, "Intention-aware motion planning," in Algorithmic foundations of robotics X: Springer, 2013, pp. 475-491.

. F. M. Noori, B. Wallace, M. Z. Uddin, and J. Torresen, "A robust human activity recognition approach using openpose, motion features, and deep recurrent neural network," in Scandinavian conference on image analysis, 2019: Springer, pp. 299-310.

. C. Sawant, "Human activity recognition with openpose and Long Short-Term Memory on real time images," EasyChair, 2516-2314, 2020.

. M. Z. Uddin and J. Torresen, "A deep learning-based human activity recognition in darkness," in 2018 Colour and Visual Computing Symposium (CVCS), 2018: IEEE, pp. 1-5.

. Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, "Realtime multi-person 2d pose estimation using part affinity fields," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7291-7299.

. Z. Cao, G. Hidalgo, T. Simon, S.-E. Wei, Y. J. I. t. o. p. a. Sheikh, and m. intelligence, "OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields," vol. 43, no. 1, pp. 172-186, 2019.

. S. Hochreiter and J. J. N. c. Schmidhuber, "Long short-term memory," vol. 9, no. 8, pp. 1735-1780, 1997.

. V. Narayanan, B. M. Manoghar, V. S. Dorbala, D. Manocha, and A. Bera, "Proxemo: Gait-based emotion learning and multi-view proxemic fusion for socially-aware robot navigation," arXiv preprint arXiv:2003.01062, 2020.

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Published

2021-05-10

How to Cite

[1]
T. Do Nam, “HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES”, JMST, no. 72A, pp. 1-12, May 2021.

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Section

Articles