Current Researches and Future Development Trend of Intelligent Robot: A Review


With the advancing of industrialization and the advent of the information age, intelligent robots play an increasingly important role in intelligent manufacturing, intelligent transportation system, the Internet of things, medical health and intelligent services. Based on working experiences in and reviews on intelligent robot studies both in China and abroad, the authors summarized researches on key and leading technologies related to human-robot collaboration, driverless technology, emotion recognition, brain-computer interface, bionic software robot and cloud platform, big data network, etc. The development trend of intelligent robot was discussed, and reflections on and suggestions to intelligent robot development in China were proposed. The review is not only meant to overview leading technologies of intelligent robot all over the world, but also provide related theories, methods and technical guidance to the technological and industrial development of intelligent robot in China.

This is a preview of subscription content, log in to check access.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.


  1. [1]

    Ji-Dong Yu, iFLY TEK: 2015 Opens a New Era for Intelligent Robot. (in Chinese)

  2. [2]

    International Federation of Robotics. Industrial Robotics Standardization. [Online], Available:

  3. [3]

    International Federation of Robotics. Industrial Robot as Defined by ISO 8373. [Online], Available:

  4. [4]

    International Federation of Robotics. Service Robots. [Online], Available:

  5. [5]

    President Obama Launches Advanced Manufacturing Partnership.

  6. [6]

    National Robotics Initiative.

  7. [7]

    A Roadmap for US Robotics-From Internet to Robotics.

  8. [8]

    National Robotics Initiative 2.0: Ubiquitous Collaborative Robots (NRI-2.0).

  9. [9]

  10. [10]

  11. [11]

    Horizon 2020 Projects.

  12. [12]

    Robotics and Autonomous Systems Cotents.

  13. [13]

    Industrie 4.0.

  14. [14]

    France Robots Initiatives.

  15. [15]

  16. [16]

    Robot Revolution Initiative.

  17. [17]

    Japan Releases “New Development Strategy of Robot”. (in Chinese)

  18. [18]

  19. [19]

    Korean Government Constructs “The Strategy Towards a Robotic Power”. (in Chinese)

  20. [20]

    Speech by Xi Jinping in 17th Academician Conference of Chinese Academy of Sciences and 12th Academician Conference of Chinese Academy of Engineering. (in Chinese)

  21. [21]

    The Development Plan of Robotic Industry (2016–2020) has been issued. (in Chinese)

  22. [22]

    W. T. Wang. Study of end effect of demographic dividend on China economic growth. Finance & Trade Economics, no. 11, pp. 14–20, 2012. (in Chinese)

    Google Scholar 

  23. [23]

    B. Lu. Global robot market welcomes rapid development in 2011. Robot Technique and Application, no. 4, pp. 10–11, 2012. DOI: 10.3969/j.issn.1004-6437.2012.04. 003. (in Chinese)

    Google Scholar 

  24. [24]

    EXPO21XX. com online exhibitions. Automation online exhibition. [Online], Available:

  25. [25]

    N. Correll, K. E. Bekris, D. Berenson, O. Brock, A. Causo, K. Hauser, K. Okada, A. Rodriguez, J. M. Romano, P. R. Wurman. Analysis and observations from the first amazon picking challenge. IEEE Transactions on Automation Science and Engineering, vol. 15, no. 1, pp. 172–188, 2018. DOI: 10.1109/TASE.2016.2600527.

    Article  Google Scholar 

  26. [26]

    S. Kammel, J. Ziegler, B. Pitzer, M. Werling, T. Gindele, D. Jagzent, J. Schröder, M. Thuy, M. Goebl, F. von Hundelshausen, O. Pink, C. Frese, C. Stiller. Team Annie-WAY’s autonomous system for the 2007 DARPA Urban Challenge. Journal of Field Robotics, vol. 25, no. 9, pp. 615–639, 2008. DOI: 10.1002/rob.20252.

    Article  Google Scholar 

  27. [27]

    S. Y. Feng, E. Whitman, X. Xinjilefu, C. G. Atkeson. Optimization-based full body control for the DARPA robotics challenge. Journal of Field Robotics, vol. 32, no. 2, pp. 293–312, 2015. DOI: 10.1002/rob.21559.

    Google Scholar 

  28. [28]

    STMD: Centennial Challenges.

  29. [29]

    The Chinese Institute of Electronics. 2017 China robotic industry development report. [Online], Available:, September 14, 2017. (in Chinese)

  30. [30]

    R. F. Li. Development strategy for China industrial robot. Aeronautical Manufacturing Technology, no. 9, pp. 32–37, 2010. Doi: 10.3969/j.issn.1671-833X.2010.09. 003. (in Chinese)

    Google Scholar 

  31. [31]

    T. M. Wang, Y. Tao, Y. Chen. Research status and development trends of the service robotic technology. Scient ia Sinica Informationis, vol. 42, no. 9, pp. 1049–1066, 2012. Doi: 10.1360/112012-402. (in Chinese)

    Google Scholar 

  32. [32]

    Rethink robotics. (in Chinese)

  33. [33]

    IRB 14000 YUMI.

  34. [34]

    Flexible Coordinated Robot. (in Chinese)

  35. [35]

    Automated warehouse systems from a single source.

  36. [36]

    Mystery Robot Revealed: RoboDynamics Luna Is Fully Programmable Adult-size Personal Robot.

  37. [37]

    A. Tapus, A. Peca, A. Aly, C. Pop, L. Jisa, S. Pintea, A. S. Rusu, D. O. David. Children with autism social engagement in interaction with Nao, an imitative robot: A series of single case experiments. Interaction Studies, vol. 13, no. 3, pp. 315–347, 2012. DOI: 10.1075/is.13.3. 01tap.

    Article  Google Scholar 

  38. [38]

    F. Tanaka, K. Isshiki, F. Takahashi, M. Uekusa, R. Sei, K. Hayashi. Pepper learns together with children: Development of an educational application. In Proceedings of the 15th International Conference on Humanoid Robots, IEEE, Seoul, South Korea, pp. 270–275, 2015. DOI: 10.1109/HUMANOIDS.2015.7363546.

    Google Scholar 

  39. [39]

    G. Metta, G. Sandini, D. Vernon, L. Natale, F. Nori. The iCub humanoid robot: an open platform for research in embodied cognition. In Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, ACM, Gaithersburg, Maryland, USA, pp. 50–56, 2008. DOI: 10.1145/1774674.1774683.

    Google Scholar 

  40. [40]

    T. Asfour, K. Yokoi, C. S. G. Lee, J. Kuffner. Humanoid robotics. IEEE Robotics & Automation Magazine, vol. 19, no. 1, pp. 108–118, 2012. DOI: 10.1109/MRA.2012. 2186688.

    Article  Google Scholar 

  41. [41]

    Leading the Future of Service Robot Application: “UU” by CANBOT Appeared at the 2017 World Robot Assembly. (in Chinese)

  42. [42]

    Ye Wang of Ninebot Make Affordable Balance Car for Common People.

  43. [43]

    PWC. Trends and research directions of medical robotics. [Online], Available: (in Chinese)

  44. [44]

    Robotic Surgery.

  45. [45]

    I. A. M. J. Broeders, J. Ruurda. Robotics revolutionizing surgery: The Intuitive Surgical “Da Vinci” system. Industrial Robot, vol. 28, no. 5, pp. 387–392, 2001. DOI: 10.1108/EUM0000000005845.

    Article  Google Scholar 

  46. [46]

    U. Hagn, M. Nickl, S. Jörg, G. Passig, T. Bahls, A. Nothhelfer, F. Hacker, L. Le-Tien, A. Albu-Schaffer, R. Konietschke, M. Grebenstein, R. Warpup, R. Haslinger, M. Frommberger, G. Hirzinger. The DLR MIRO: A versatile lightweight robot for surgical applications. Industrial Robot, vol. 35, no. 4, pp. 324–336, 2008. DOI: 10.1108/01439910810876427.

    Article  Google Scholar 

  47. [47]

    ViRob Life in Motion.

  48. [48]

    Z. M. Tian, W. S. Lu, T. M. Wang, B. L. Ma, Q. J. Zhao, G. L. Zhang. Application of a robotic telemanipulation system in stereotactic surgery. Stereotactic and Functional Neurosurgery, vol. 86, no. 1, pp. 54–61, 2008. DOI: 10.1159/000110742.

    Article  Google Scholar 

  49. [49]

    Y. S. Sun, D. M. Wu, Z. J. Du, L. N. Sun. Robot-assisted needle insertion strategies based on liver force model. Robot, vol. 33, no. 1, pp. 66–70, 2001. DOI: 10.3724/SP.J. 1218.2011.00066. (in Chinese)

    Article  Google Scholar 

  50. [50]

    S. X. Wang, X. F. Wang, J. X. Zhang, X. M. Jiang, J. M. Li. A new auxiliary celiac minimally invasive surgery robot: “MicroHandA”. Robot Technique and Application, no. 4, pp. 17–21, 2011. DOI: 10.3969/j.issn.1004-6437.2011. 04.005. (in Chinese)

    Google Scholar 

  51. [51]

    Capsule Endoscope. (in Chinese)

  52. [52]

    L. Zhou, Y. Wang, B. B. Wang, X. Y. Li, Y. Feng. Efficiency evaluation of a robotic navigation system for femoral neck surgery in clinical trials by data envelopment analys is. Beijing Biomedical Engineering, vol. 33, no. 6, pp. 614–619, 2014. DOI: 10.3969/j.issn.1002-3208. 2014.06.11.

    Google Scholar 

  53. [53]

    H. F. Yang, Z. M. Tian, Y. C. Sun, G. Cui, B. Li, Z. B. Zhang, Y. J. Piao, F. Q. Zhang. Clinical Application of the Sixth Generation Neurosurgical Robot Remebot. Chinese Journal for Clinicians, vol. 45, no. 3, pp. 86–88, 2017. DOI: 10.3969/j.issn.2095-8552.2017.03.030. (in Chinese)

    Google Scholar 

  54. [54]

    Army Orders Up 315 Recon Scout XT Robots From ReconRobotics.

  55. [55]

    Zephyr UAV Continues to Break Records on First Authorized Civil Flight.

  56. [56]

    GuardianTM S.

  57. [57]

    Ocean One Lands on the Moon.

  58. [58]

    S. Kuindersma, R. Deits, M. Fallon, A. Valenzuela, H. K. Dai, F. Permenter, T. Koolen, P. Marion, R. Tedrake. Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot. Autonomous Robots, vol. 40, no. 3, pp. 429 455, 2016. DOI: 10.1007/s10514-015-9479-3.

  59. [59]

    China Has Developed Rescue Robot width the Ability of Life Detection. (in Chinese)

  60. [60] (Robot Automation Equipment) (in Chinese)

  61. [61]

    Jiaolong Completed Its 38th Travel on the Chinese Ocean. (in Chinese)

  62. [62]


  63. [63]

    Intel Predicts Autonomous Driving Will Spur New ‘Passenger Economy’ Worth $7 Trillion.

  64. [64]

    We Drive Every Day on Public Roads So We can Build A Safer Driver.

  65. [65]

    Full Self-Driving Hardware on All Cars.

  66. [66]

    Volvo Is Sticking with Uber to Win the Autonomous driving ‘marathon’.

  67. [67]

    Uber wanted to revolutionize trucking like it did taxis-but it hasn’t made a dent.

  68. [68]

    Apple’s autonomous car tech is ‘where Google was three years ago’ says someone who has seen it.

  69. [69]

    Intel’s $15 Billion Purchase of Mobileye Shakes up Driverless Car Sector.

  70. [70]

    Behind the Big Apollo Project: Baidu Map Makes Travel Simpler. (in Chinese)

  71. [71]

    The Horizon Compang Established Its Shanghai Autonomous Driving Research Center Accelerating the Completion of Hugo System. (in Chinese)

  72. [72]

    Z. H. Lin, T. L. Xu. Application of robot technology in logistics industry. Logistics Technology, vol. 31, no. 7, pp. 42–45, 2012. DOI: 10.3969/j.issn.1005-152X.2012.07. 013. (in Chinese)

    Google Scholar 

  73. [73]

    B. W. Shen, N. B. Yu, J. T. Liu. Intelligent scheduling and path planning of warehouse mobile robots. CAAI Transactions on Intelligent Systems, vol. 9, no. 6, pp. 659–664, 2014. DOI: 10.3969/j.issn.1673-4785.201312048. (in Chinese)

    Google Scholar 

  74. [74]

    Meet Amazon’s Busiest Employee--the Kiva Robot.

  75. [75] (Chinese Logistic Robot Geek+ Steps into Japan Market) (in Chinese)

  76. [76]

    Quicktron Finished B Round Capital Raising of 0.2 Billion RMB, Attracting the First Inverstment from Cainiao. (in Chinese)

  77. [77]

    Delivery Sorting Robot: A Cute and Fantastic Robot. (in Chinese)

  78. [78]

    Warehousing and Logistics Robot Shipments Will Reach 620 000 Units Annually by 2021.

  79. [79]

    Amazon Claims First Successful Prime Air Drone Delivery. [Online], Available:, December 14, 2016.

  80. [80]

    YARA and KONGSBERG enter into partnership to build world’s first autonomous and zero emissions ship.

  81. [81]

    Robots and Robotic Devices-Collaborative Robots, ISO/TS 15066, 2016.

  82. [82]

    S. Wolf, G. Hirzinger. A new variable stiffness design: Matching requirements of the next robot generation. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Pasadena, USA, pp. 1741–1746, 2008. DOI: 10.1109/ROBOT.2008.4543452.

    Google Scholar 

  83. [83]

    J. Choi, S. Hong, W. Lee, S. Kang, M. Kim. A robot joint with variable stiffness using leaf springs. IEEE Transactions on Robotics, vol. 27, no. 2, pp. 229–238, 2011. DOI: 10.1109/TRO.2010.2100450.

    Article  Google Scholar 

  84. [84]

    S. Wolf, O. Eiberger, G. Hirzinger. The DLR FSJ: Energy based design of a variable stiffness joint. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Shanghai, China, pp. 5082–5089, 2011. DOI: 10.1109/ICRA.2011.5980303.

    Google Scholar 

  85. [85]

    A. M. Zanchettin, N. M. Ceriani, P. Rocco, H. Ding, B. Matthias. Safety in human-robot collaborative manufacturing environments: Metrics and control. IEEE Transactions on Automation Science and Engineering, vol. 13, no. 2, pp. 882–893, 2016. DOI: 10.1109/TASE.2015. 2412256.

    Article  Google Scholar 

  86. [86]

    M. Zinn, O. Khatib, B. Roth. A new actuation approach for human friendly robot design. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, New Orleans, USA, vol. 1, pp. 249–254, 2004. DOI: 10.1109/ROBOT.2004.1307159.

    Google Scholar 

  87. [87]

    J. S. Gutmann, M. Fukuchi, M. Fujita. 3D perception and environment map generation for humanoid robot navigation. International Journal of Robotics Research, vol. 27, no. 10, pp. 1117–1134, 2008. DOI: 10.1177/0278364908096316.

    Article  Google Scholar 

  88. [88]

    A. Schmitz, P. Maiolino, M. Maggiali, L. Natale, G. Cannata, G. Metta. Methods and technologies for the implementation of large-scale robot tactile sensors. IEEE Transactions on Robotics, vol. 27, no. 3, pp. 389–400, 2011. DOI: 10.1109/TRO.2011.2132930.

    Article  Google Scholar 

  89. [89]

    A. Fanaei, M. Farrokhi. Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment. Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 125–144, 2006.

    MATH  Google Scholar 

  90. [90]

    H. Masuta, N. Kubota. Information reduction for environment perception of an intelligent robot arm equipped with a 3D range camera. In Proceedings of SICE Annual Conference, IEEE, Taipei, China, pp. 392–397, 2010.

    Google Scholar 

  91. [91]

    A. J. Davison, I. D. Reid, N. D. Molton, O. Stasse. Mono-SLAM: Real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052–1067, 2007. DOI: 10.1109/TPAMI. 2007.1049.

    Article  Google Scholar 

  92. [92]

    M. Blösch, S. Weiss, D. Scaramuzza, R. Siegwart. Vision based MAV navigation in unknown and unstructured environments. IEEE In Proceedings of International Conference on Robotics and Automation, IEEE, Anchorage, USA, pp. 21–28, 2010. DOI: 10.1109/ROBOT.2010. 5509920.

    Google Scholar 

  93. [93]

    Z. Y. Liu. The Theory of Intelligent Traffic Control and Application, Beijing, China: Science Press, 2003. (in Chinese)

    Google Scholar 

  94. [94]

    M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. K. Zhang, X. Zhang, J. Zhao, K. Zieba. End to end learning for self-driving cars. arXiv preprint arXiv: 1604. 07316, 2016.

    Google Scholar 

  95. [95]

    S. Liu. The First Technical Book of Unmanned Driving, Beijing, China: Publishing House of Electronics Industry, 2017. (in Chinese)

    Google Scholar 

  96. [96]

    R. Olfati-Saber, J. A. Fax, R. M. Murray. Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, vol. 95, no. 1, pp. 215–233, 2007. DOI: 10.1109/JPROC.2006.887293.

    Article  MATH  Google Scholar 

  97. [97]

    G. D. Shi, K. H. Johansson. Multi-agent robust consensus — Part I: Convergence analysis. In Proceeding of the 50th Decision and Control and European Control Conference, IEEE, Orlando, USA, pp. 5744–5749, 2011. DOI: 10.1109/CDC.2011.6160957.

    Google Scholar 

  98. [98]

    Y. G. Sun, L. Wang, G. M. Xie. Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays. Systems & Control Letters, vol. 57, no. 2, pp. 175–183, 2008. DOI: 10.1016/j. sysconle.2007.08.009.

    MathSciNet  Article  MATH  Google Scholar 

  99. [99]

    M. Brambilla, E. Ferrante, M. Birattari, M. Dorigo. Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, vol. 7, no. 1, pp. 1–41, 2013. DOI: 10.1007/s11721-012-0075-2.

    Article  Google Scholar 

  100. [100]

    H. T. Xue, Y. Y. Ye, L. C. Shen, W. S. Chang. A roadmap of multi-agent system architecture and coordination research. Robot, vol. 23, no. 1, pp. 85–90, 2001. DOI: 10.3321/j.issn:1002-0446.2001.01.017. (in Chinese)

    Google Scholar 

  101. [101]

    M. Flint, M. Polycarpou, E. Fernandez-Gaucherand. Cooperative control for multiple autonomous UAV’s searching for targets. In Proceeding of the 41st IEEE Conference on Decision and Control, IEEE, Las Vegas, NV, USA, vol. 3, pp. 2823–2828, 2003. DOI: 10.1109/CDC.2002. 1184272.

    Article  Google Scholar 

  102. [102]

    A. T. Hafez, A. J. Marasco, S. N. Givigi, M. Iskandarani, S. Yousefi, C. A. Rabbath. Solving multi-UAV dynamic encirclement via model predictive control. IEEE Transactions on Control Systems Technology, vol. 23, no. 6, pp. 2251–2265, 2015. DOI: 10.1109/TCST.2015.2411632.

    Google Scholar 

  103. [103]

    A. L. Yang, W. Naeem, M. R. Fei, L. Liu, X. W. Tu. Multiple robots formation manoeuvring and collision avoidance strategy. International Journal of Automation and Computing, vol. 14, no. 6, pp. 696–705, 2017. DOI: 10. 1007/s11633-016-1030-2.

    Article  Google Scholar 

  104. [104]

    Y. Zhang, S. L. Luo. Recognizing and expressing affect. Computer Engineering and Applications, vol. 39, no. 33, pp. 98–102, 2003. DOI: 10.3321/j.issn:1002-8331.2003.33. 033. (in Chinese)

    Google Scholar 

  105. [105]

    M. Merras, S. El Hazzat, A. Saaidi, K. Satori, A. G. Nazih. 3D face reconstruction using images from cameras with varying parameters. International Journal of Automation and Computing, vol. 14, no. 6, pp. 661–671, 2017. DOI: 10.1007/s11633-016-0999-x.

    Article  Google Scholar 

  106. [106]

    E. Cambria. Affective computing and sentiment analysis. IEEE Intelligent Systems, vol. 31, no. 2, pp. 102–107, 2016. DOI: 10.1109/MIS.2016.31.

    Article  Google Scholar 

  107. [107]

    A. Bartels, S. Zeki. The neural correlates of maternal and romantic love. NeuroImage, vol. 21, no. 3, pp. 1155–1166, 2004. DOI: 10.1016/j.neuroimage.2003.11.003.

    Article  Google Scholar 

  108. [108]

    J. Lin, H. Yu, C. Y. Miao, Z. Q. Shen. An affective agent for studying composite emotions. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, ACM, Istanbul, Turkey, pp. 1947–1948, 2015.

    Google Scholar 

  109. [109]

    Z. H. Zeng, J. L. Tu, B. M. Pianfetti, T. S. Huang. Audio-visual affective expression recognition through multistream fused HMM. IEEE Transactions on Multimedia, vol. 10, no. 4, pp. 570–577, 2008. DOI: 10.1109/TMM. 2008.921737.

    Google Scholar 

  110. [110]

    S. L. Happy, A. Routray. Automatic facial expression recognition using features of salient facial patches. IEEE Transactions on Affective Computing, vol. 6, no. 1, pp. 1–12, 2015. DOI: 10.1109/TAFFC.2014.2386334.

    Article  Google Scholar 

  111. [111]

    Z. H. Zeng, M. Pantic, G. I. Roisman, T. S. Huang. A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 1, pp. 39–58, 2009. DOI: 10.1109/TPAMI.2008.52.

    Article  Google Scholar 

  112. [112]

    G. Santhanam, S. I. Ryu, B. M. Yu, A. Afshar, K. V. Shenoy. A high-performance brain-computer interface. Nature, vol. 442, no. 7099, pp. 195–198, 2006. DOI: 10. 1038/nature04968.

    Article  Google Scholar 

  113. [113]

    J. Dobson. Remote control of cellular behaviour with magnetic nanoparticles. Nature Nanotechnology, vol. 3, no. 3, pp. 139–143, 2008. DOI: 10.1038/nnano.2008.39.

    MathSciNet  Article  Google Scholar 

  114. [114]

    L. R. Hochberg, D. Bacher, B. Jarosiewicz, N. Y. Masse, J. D. Simeral, J. Vogel, S. Haddadin, J. Liu, S. S. Cash, P. van der Smagt, J. P. Donoghue. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, vol. 485, no. 7398, pp. 372–375, 2012. DOI: 10.1038/nature11076.

    Article  Google Scholar 

  115. [115]

    M. J. Vansteensel, E. G. M. Pels, M. G. Bleichner, M. P. Branco, T. Denison, Z. V. Freudenburg, P. Gosselaar, S. Leinders, T. H. Ottens, M. A. van den Boom, P. C. van Rijen, E. J. Aarnoutse, N. F. Ramsey. Fully implanted Brain-computer interface in a locked-in patient with ALS. New England Journal of Medicine, vol. 375, no. 21, pp. 2060–2066, 2016. DOI: 10.1056/NEJMoa1608085.

    Article  Google Scholar 

  116. [116]

    C. E. Bouton, A. Shaikhouni, N. V. Annetta, M. A. Bockbrader, D. A. Friedenberg, D. M. Nielson, G. Sharma, P. B. Sederberg, B. C. Glenn, W. J. Mysiw, A. G. Morgan, M. Deogaonkar, A. R. Rezai. Restoring cortical control of functional movement in a human with quadriplegia. Nature, vol. 533, no. 7602, pp. 247–250, 2016. DOI: 10.1038/nature17435.

    Article  Google Scholar 

  117. [117]

    F. R. Willett, C. Pandarinath, B. Jarosiewicz, B. A. Murphy, W. D. Memberg, C. H. Blabe, J. Saab, B. L. Walter, J. A. Sweet, J. P. Miller, J. M. Henderson, K. V. Shenoy, J. D. Simeral, L. R. Hochberg, R. F. Kirsch, A. B. Ajiboye. Feedback control policies employed by people using intracortical brain-computer interfaces. Journal of Neural Engineering, vol. 14, no. 1, Article number 016001, 2016. DOI: 10.1088/1741–2560/14/1/016001.

    Google Scholar 

  118. [118]

    A. B. Ajiboye, F. R. Willett, D. R. Young, W. D. Memberg, B. A. Murphy, J. P. Miller, B. L. Walter, J. A. Sweet, H. A. Hoyen, M. W. Keith, P. H. Peckham, J. D. Simeral, J. P. Donoghue, L. R. Hochberg, R. F. Kirsch. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration. The Lancet, vol. 389, no. 10081, pp. 1821–1830, 2017. DOI: 10.1016/S0140-6736(17)30601-3.

    Article  Google Scholar 

  119. [119]

    X. J. Zhu, A. B. Goldberg, R. Brachman, T. Dietterich. Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 3, no. 1, pp. 1–130, 2009.

    Article  Google Scholar 

  120. [120]

    P. Englert, A. Paraschos, M. P. Deisenroth, J. Peters. Probabilistic model-based imitation learning. Adaptive Behavior, vol. 21, no. 5, pp. 388–403, 2013. DOI: 10.1177/1059712313491614.

    Article  Google Scholar 

  121. [121]

    B. D. Argall, S. Chernova, M. Veloso, B. Browning. A survey of robot learning from demonstration. Robotics and Autonomous Systems, vol. 57, no. 5, pp. 469–483, 2009. DOI: 10.1016/j.robot.2008.10.024.

    Article  Google Scholar 

  122. [122]

    S. M. Khansari-Zadeh, A. Billard. Learning stable nonlinear dynamical systems with Gaussian mixture models. IEEE Transactions on Robotics, vol. 27, no. 5, pp. 943–957, 2011. DOI: 10.1109/TRO.2011.2159412.

    Article  Google Scholar 

  123. [123]

    J. Kober, K. Mülling, O. Krömer, C. H. Lampert, B. Schölkopf, J. Peters. Movement Templates for Learning of Hitting and Batting. In Proceedings of International Conference on Robotics and Automation, IEEE, Anchorage, AK, USA, pp. 853–858, 2010. DOI: 10.1109/ROBOT. 2010.5509672.

    Google Scholar 

  124. [124]

    S. Levine, P. Pastor, A. Krizhevsky, D. Quillen. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. arXiv preprint arXiv: 1603.02199, 2016.

    Google Scholar 

  125. [125]

    C. Finn, S. Levine. Deep visual foresight for planning robot motion. In Proceedings of International Conference on Robotics and Automation, IEEE, Singapore, pp. 2786–2793, 2017. DOI: 10.1109/ICRA.2017.7989324.

    Google Scholar 

  126. [126]


  127. [127]

    SFG Series Flexible Gripping Jaw. (in Chinese)

  128. [128]

    T. Ranzani, G. Gerboni, M. Cianchetti, A. Menciassi. A bioinspired soft manipulator for minimally invasive surgery. Bioinspiration & Biomimetics, vol. 10, no. 3, Article number 035008, 2015. DOI: 10.1088/1748-3190/10/3/035008.

    Google Scholar 

  129. [129]

    M. Luo, W. J. Tao, F. C. Chen, T. K. Khuu, S. Ozel, C. D. Onal. Design improvements and dynamic characterization on fluidic elastomer actuators for a soft robotic snake. In Proceedings of International Conference on Technologies for Practical Robot Applications, IEEE, Woburn, USA, 2014. DOI: 10.1109/TePRA.2014.6869154.

    Google Scholar 

  130. [130]

    R. Deimel, O. Brock. A novel type of compliant and underactuated robotic hand for dexterous grasping. The International Journal of Robotics Research, vol. 35, no. 1–3, pp. 161–185, 2016. DOI: 10.1177/0278364915592961.

    Google Scholar 

  131. [131]

    M. Rolf, J. J. Steil. Constant curvature continuum kinematics as fast approximate model for the Bionic Handling Assistant. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Vilamoura, Portugal, pp. 3440–3446, 2012. DOI: 10.1109/IROS.2012.6385596.

    Google Scholar 

  132. [132]

    J. T. Lei, H. Y. Yu, T. M. Wang. Dynamic bending of bionic flexible body driven by pneumatic artificial muscles (PAMs) for spinning gait of quadruped robot. Chinese Journal of Mechanical Engineering, vol. 29, no. 1, pp. 11–20, 2016. DOI: 10.3901/CJME.2015.1016.123.

    Article  Google Scholar 

  133. [133]

    C. Laschi, M. Cianchetti, B. Mazzolai, L. Margheri, M. Follador, P. Dario. Soft robot arm inspired by the octopus. Advanced Robotics, vol. 26, no. 7, pp. 709–727, 2012. DOI: 10.1163/156855312X626343.

    Article  Google Scholar 

  134. [134]

    D. M. Aukes, B. Heyneman, J. Ulmen, H. Stuart, M. R. Cutkosky, S. Kim, P. Garcia, A. Edsinger. Design and testing of a selectively compliant underactuated hand. The International Journal of Robotics Research, vol. 33, no. 5, pp. 721–735, 2014. DOI: 10.1177/0278364913518997.

    Article  Google Scholar 

  135. [135]

    J. J. Kuffner, S. M. LaValle. Space-filling trees: A new perspective on incremental search for motion planning. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, San Francisco, USA, pp. 2199–2206, 2011. DOI: 10.1109/IROS.2011. 6094740.

    Google Scholar 

  136. [136]

    G. H. Tian, Y. W. Xu. Cloud robotics: Concept, architectures and key technologies. Journal of Shandong University (Engineering Science) vol. 44, no. 6, pp. 47–54, 2014. DOI: 10.6040/j.issn.1672-3961.0.2014.282. (in Chinese)

    Google Scholar 

  137. [137]

    M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote, J. Leibs, E. Berger, R. Wheeler, A. Ng. ROS: An opensource robot operating system. ICRA Workshop on Open Source Software, vol. 3, no. 3, 2009.

    Google Scholar 

  138. [138]

    M. Yuriyama, T. Kushida. Sensor-cloud infrastructure-physical sensor management with virtualized sensors on cloud computing. In Proceedings of the 13th International Conference on Network-Based Information Systems IEEE, Takayama, Japan, pp. 1–8, 2010. DOI: 10.1109/NBiS.2010.32.

    Google Scholar 

  139. [139]

    S. Nakagawa, N. Igarashi, Y. Tsuchiya, M. Narita, Y. Kato. An implementation of a distributed service framework for cloud-based robot services. In Proceedings of the 38th Annual Conference on IEEE Industrial Electronics Society. IEEE, Montreal, Canada, pp. 4148–4153, 2012. DOI: 10.1109/IECON.2012.6389225.

    Google Scholar 

  140. [140]

    L. Turnbull, B. Samanta. Cloud robotics: Formation control of a multi robot system utilizing cloud infrastructure. In Proceedings of IEEE Southeastcon IEEE, Jacksonville, FL, USA, pp. 1, 2013. DOI: 10.1109/SECON.2013. 6567422.

    Google Scholar 

  141. [141]

    B. Kehoe, A. Matsukawa, S. Candido, J. Kuffner, K. Goldberg. Cloud-based robot grasping with the Google object recognition engine. In Proceedings of International Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, pp. 4263–4270, 2013. DOI: 10.1109/ICRA.2013.6631180.

    Google Scholar 

  142. [142]

    D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis. Mastering the game of go with deep neural networks and tree search. Nature, vol. 529, no. 7587, pp. 484–489, 2016. DOI: 10.1038/nature16961.

    Article  Google Scholar 

Download references


This work was supported by the Chinese MIIT Intelligent Manufacturing and New Mode Application “Application of new mode of intelligent manufacturing of Chinese medicine products”. The authors would like to extend heartfelt thanks to Jin-Chang Liu, a researcher from High Technology Research and Development Center, for his nice help and constructive suggestions. The authors′ gratitudes also go to other specialists in the robotic field who have made great contributions to this work, including Tian-Ran Wang, Ba Zhang, He-Gao Cai, Han Ding, Ning Xi, Ze-Xiang Li, Jie Zhao, Min Tan, Tian Huang, Qiang Huang, Li-Ning Sun, Yao-Nao Hou, Cheng-Liang Liu, Ya-Ping Jin, Jian-Da Han, Dao-Kui Qu, Fang Xu, Jing-Tai Liu, Zeng-Guang Hou, Cai-Hua Xiong, Yong-Chun Fang, Xing-Guan Duan, Dian-Sheng Chen, Rong Xiong, Yong- Sheng Ou, et al.

Author information



Corresponding author

Correspondence to Yong Tao.

Additional information

Yong Tao received the Ph. D. degree in School of Mechanical Engineering and Automation, Beihang University, China in 2009. Currently, he is an associate professor at Beihang University, China. He has published about 30 refereed journal and conference papers. He also participated in compiling and finishing 5 books in the robotic field. He received Second Prize of Machinery Industry Science and Technology Award, and Second Prize of Jiangsu Science and Technology Progress Award. He received the honorary title of “excellent worker of the Chinese Institute of Electronics” in 2014, and one of the excellent scientific papers of the second China Association for Science and Technology. He is a member of Chinese Institute of Electronics Embedded Systems and Robotics Branch, a member of the robotics Association of the Mechanical Engineering Society.

His research interests include intelligent robot advanced control technology and integrated applications, control of embedded mechanical and electrical integration, intelligent manufacturing development strategy consulting.

Hui Liu received the B. Sc. degree in School of Engineering, Southwest Jiaotong University, China in 2015. He is currently a master student in School of Mechanical Engineering & Automation, Beihang University, China.

His research interests include motion planning and generating based on demonstration, self-learning of grasping and machine vision.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, T., Tao, Y. & Liu, H. Current Researches and Future Development Trend of Intelligent Robot: A Review. Int. J. Autom. Comput. 15, 525–546 (2018).

Download citation


  • Intelligent robot
  • human-robot collaboration
  • driverless technology
  • emotion recognition
  • brain-computer interface
  • big data network