ROBOT TASK RECOGNITION USING DEEP CONVOLUTIONAL LONG SHORT-TERM MEMORY, 106-113. SI

M. S. Midhun and James Kurian

References

  1. [1] L.-M. Li, J. Zhao, and C.-J. Yan, Robot-to-human handoverin a comfortable and friendly manner, Mechatronic Systemsand Control, 46, 2018, 1–7.
  2. [2] A. Kumar, Reinforcement learning: Application and advancestowards stable control strategies, Mechatronic Systems andControl, 51, 2023, 53–57.
  3. [3] Q. Lu, Y. Chen, X. Qi, and L. Liu, Improved particle swarmalgorithm for cooperative multi-task allocation of heterogeneousUAVs, Mechatronic Systems and Control, 51, 2023, 42–52.
  4. [4] V. Trieu Minh, R. Moezzi, J. Cyrus, and J. Hlava, Feasibleand optimal trajectories generation for autonomous drivingvehicles, Mechatronic Systems and Control, 51, 2023, 11–24.
  5. [5] C. Pu, J. Ren, Y. Zhai, and Y. Zhang, Trajectory trackingof nonholonomic constraint mobile robot based on ADRC,Mechatronic Systems and Control, 51, 2023, 1–9.
  6. [6] W. Touzout, D. Benazzouz, and Y. Benmoussa, Mobile robotenergy modelling integrated into ROS and gazebo-basedsimulation environment, Mechatronic Systems and Control, 50,2022, 66–73.
  7. [7] A. Krizhevsky, I. Sutskever, and G.E. Hinton, ImageNetclassification with deep convolutional neural networks, inAdvances in neural information processing systems, 25. (RedHook, NY: Curran, 2012), 112–116.
  8. [8] G. Van Houdt, C. Mosquera, and G. N´apoles, A review on thelong short-term memory model, Artificial Intelligence Review,53(8), 2020, 5929–5955.
  9. [9] M. Ozaki, Y. Adachi, Y. Iwahori, and N. Ishii, Survey on stillimage based human action recognition, Pattern Recognition,47(10), 2014, 3343–3361.
  10. [10] S.-R. Ke, H.L.U. Thuc, Y.-J. Lee, J.-N. Hwang, J.-H. Yoo,and K.-H. Choi, A review on video-based human activityrecognition, Computers, 2(2), 2013, 88–131.
  11. [11] Z. Cao, G. Hidalgo, T. Simon, S.-E. Wei, and Y. Sheikh,OpenPose: Realtime multi-person 2D pose estimation usingpart affinity fields, IEEE Transactions on Pattern Analysisand Machine Intelligence, 43(1), 2021, 172–186.
  12. [12] F.M. Noori, B. Wallace, M.Z. Uddin, and J. Torresen, Arobust human activity recognition approach using OpenPose,motion features, and deep recurrent neural network, Proc.Scandinavian Conf. Image Analysis, Norrk¨oping, 2019, pp.299–310.
  13. [13] M.Z. Uddin and J. Torresen, A deep learning-based humanactivity recognition in darkness, Proc. Colour and VisualComputing Symp. (CVCS), Gjovik, 2018, 1–5.
  14. [14] M. Jung and S. Chi, Human activity classification based onsound recognition and residual convolutional neural network,Automation in Construction, 114, 2020, 103177.
  15. [15] J. Yang, M. Ma, Y. Fu, and Y. Gu, LSTM-attentiontext classification method combined with key information,Mechatronic Systems and Control, 50, 2022, 1–7
  16. [16] I. Iturrate, E. Roberge, E.H. Østergaard, V. Duchaine, andT.R. Savarimuthu, Improving the generalizability of robotassembly tasks learned from demonstration via CNN-basedsegmentation, Proc. IEEE 15th Int. Conf. Automation Scienceand Engineering (CASE), Vancouver, BC, 2019, 553–560.
  17. [17] S. Hak, N. Mansard, and O. Stasse, Humanoid robot taskrecognition from movement analysis, Proc. 10th IEEE-RASInt. Conf. Humanoid Robots, Nashville, TN, 2010, 314–321.
  18. [18] T. Beysolow, II, Applied natural language processing withpython: implementing machine learning and deep learningalgorithms for natural language processing. (Berkeley, CA:Apress, 2018).
  19. [19] D.P. Kingma and J. Ba, Adam: A method for stochasticoptimisation, arXiv preprint arXiv:1412.6980, 2014.
  20. [20] W. Falcon, Pytorch lightning, 2019. [Online]. Available:https://github.com/ PytorchLightning/pytorch-lightning
  21. [21] P. Corke, Robot arm kinematics, in Robotics, Vision andControl. (Berlin: Springer, 2017), 193–228.
  22. [22] P. Corke and J. Haviland, Not your grandmother’s toolbox—The robotics toolbox reinvented for python, Proc. IEEEInt. Conf. Robotics and Automation (ICRA), Xi’an, 2021,11357–11363.
  23. [23] S. Cheema, S. Gulwani, and J. LaViola, QuickDraw: Improvingdrawing experience for geometric diagrams, Proc. SIGCHIConf. Human Factors in Computing Systems, New York, NY,2012, 1037–1064.
  24. [24] A. Koubaa et al, Robot Operating System (ROS), 1. (Cham:Springer, 2017).
  25. [25] M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs,R. Wheeler, and A.Y. Ng, ROS: An open-source robot operatingsystem, Proc. ICRA Workshop on Open Source Software, 3.Kobe, 2009, 5.
  26. [26] S. Chitta, I. Sucan, and S. Cousins, Moveit! [ROS topics],IEEE Robotics & Automation Magazine, 19(1), 2012, 18–19.
  27. [27] D.M. Powers, Evaluation: From precision, recall and F-measureto ROC, informedness, markedness and correlation, arXivpreprint arXiv:2010.16061, 2020.

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