Journal of Intelligent & Robotic Systems

, Volume 85, Issue 1, pp 27–45 | Cite as

Inverse Kinematics Based Human Mimicking System using Skeletal Tracking Technology

  • Mina AlibeigiEmail author
  • Sadegh Rabiee
  • Majid Nili Ahmadabadi


Humanoid robots needs to have human-like motions and appearance in order to be well-accepted by humans. Mimicking is a fast and user-friendly way to teach them human-like motions. However, direct assignment of observed human motions to robot’s joints is not possible due to their physical differences. This paper presents a real-time inverse kinematics based human mimicking system to map human upper limbs motions to robot’s joints safely and smoothly. It considers both main definitions of motion similarity, between end-effector motions and between angular configurations. Microsoft Kinect sensor is used for natural perceiving of human motions. Additional constraints are proposed and solved in the projected null space of the Jacobian matrix. They consider not only the workspace and the valid motion ranges of the robot’s joints to avoid self-collisions, but also the similarity between the end-effector motions and the angular configurations to bring highly human-like motions to the robot. Performance of the proposed human mimicking system is quantitatively and qualitatively assessed and compared with the state-of-the-art methods in a human-robot interaction task using Nao humanoid robot. The results confirm applicability and ability of the proposed human mimicking system to properly mimic various human motions.


Imitation learning Mimicry Human-robot interaction Humanoid robot Nao humanoid robot Microsoft kinect sensor 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Almetwally, I., Mallem, M.: Real-time tele-operation and tele-walking of humanoid Robot Nao using Kinect Depth Camera. In: 10th IEEE international conference on networking, sensing and control (ICNSC) 2013, pp. 463-466Google Scholar
  2. 2.
    Ningjia, Y., Feng, D., Yudi, W., Chuang, L., Tan, J.T.C., Binbin, X., Jin, Z.: A study of the human-robot synchronous control system based on skeletal tracking technology. In: IEEE International Conference on Robotics and Biomimetics (ROBIO) 2013, pp. 2191-2196Google Scholar
  3. 3.
    Siscart, M.J.R., Gibert, M.G., Alenyá, G., Industrial, I.D.R.I.I.: Algorithms and graphic interface design to control and teach a humanoid robot through human imitation. Universitat Politécnica de Catalunya (2011)Google Scholar
  4. 4.
    Luo, R.C., Shih, B.-H., Lin, T.-W.: Real time human motion imitation of anthropomorphic dual arm robot based on Cartesian impedance control. In: IEEE international symposium on robotic and sensors environments (ROSE) 2013, pp. 25-30Google Scholar
  5. 5.
    Mota, E., Moreira, A.P., do Nascimento, T.P.: Motion and Teaching of a NAO Robot. Provas de Dissertacao do MIEEC, Portugal (2011)Google Scholar
  6. 6.
    Wang, F., Cheng, T., Yongsheng, O., Yangsheng, X.: A real-time human imitation system. In: 10th World Congress on Intelligent Control and Automation (WCICA), 6-8 July 2012, pp. 3692-3697Google Scholar
  7. 7.
    Kurt, B.: Imitation of human arm movements by a humanoid robot using monocular vision. Bogaziçi University, Master of Science (2009)Google Scholar
  8. 8.
    Billard, A., Calinon, S., Dillmann, R., Schaal, S.: Handbook of Robotics Chapter 59: Robot Programming by Demonstration. Handbook of Robotics Springer (2008)Google Scholar
  9. 9.
    Kemp, C.C., Edsinger, A., Torres-Jara, E.: Challenges for robot manipulation in human environments. IEEE Robot. Autom. Mag. 14(1), 20–29 (2007)CrossRefGoogle Scholar
  10. 10.
    Li, Z., Yang, C., Su, C. -Y., Deng, S., Sun, F., Zhang, W.: Decentralized fuzzy control of multiple cooperating robotic manipulators with impedance interaction. IEEE Trans. Fuzzy Syst. 23(4), 1044–1056 (2015)CrossRefGoogle Scholar
  11. 11.
    He, W., Chen, Y., Yin, Z.: Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Transactions on Cybernetics 46(3), 620–629 (2016)CrossRefGoogle Scholar
  12. 12.
    He, W., David, A.O., Yin, Z., Sun, C.: Neural network control of a robotic manipulator with input deadzone and output constraint. IEEE Trans. Syst. Man Cybern. Syst. Hum. 99, 1–12 (2016)Google Scholar
  13. 13.
    He, W., Dong, Y., Sun, C.: Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation. IEEE Trans. Syst. Man Cybern. Syst. Hum. 46(3), 334–344 (2016). doi: 10.1109/TSMC.2015.2429555 CrossRefGoogle Scholar
  14. 14.
    Zannatha, J.M.I., Tamayo, A.J.M., Sanchez, A.D.G., Delgado, J.E.L., Cheu, L.E.R., Arevalo, W.A.S.: Development of a system based on 3D vision, interactive virtual environments, ergonometric signals and a humanoid for stroke rehabilitation. Comput. Methods Prog. Biomed 112(2), 239–249 (2013)CrossRefGoogle Scholar
  15. 15.
    Microsoft: Kinect for Windows. (2015)Google Scholar
  16. 16.
    Aldebaran: NAO Humanoid Robot. 2015
  17. 17.
    Krüger, B., Baumann, J., Abdallah, M., Weber, A.: A Study On Perceptual Similarity of Human Motions. In: Workshop on Virtual Reality Interaction and Physical Simulation (VRIPHYS) 2011, pp. 65-72Google Scholar
  18. 18.
    Tang, J.K., Leung, H., Komura, T., Shum, H.P.: Emulating human perception of motion similarity. Comput. Anim. Virtual Worlds 19(3-4), 211–221 (2008)CrossRefGoogle Scholar
  19. 19.
    Zuher, F., Romero, R.: Recognition of Human Motions for Imitation and Control of a Humanoid Robot. In: Brazilian robotics symposium and latin american robotics symposium (SBR-LARS), 16-19 Oct. 2012, pp. 190-195Google Scholar
  20. 20.
    Riley, M., Ude, A., Wade, K., Atkeson, C.G.: Enabling real-time full-body imitation: a natural way of transferring human movement to humanoids. In: IEEE international conference on robotics and automation (ICRA’03) 2003, pp. 2368-2374Google Scholar
  21. 21.
    Ude, A., Man, C., Riley, M., Atkeson, C.G.: Automatic generation of kinematic models for the conversion of human motion capture data into humanoid robot motion. In: Proceedings of the first IEEE-RAS conference on humanoid robotics (Humanoids) 2000, pp. 1-9Google Scholar
  22. 22.
    Do, M., Azad, P., Asfour, T., Dillmann, R.: Imitation of human motion on a humanoid robot using non-linear optimization. In: 8th IEEE-RAS international conference on humanoids 2008, pp. 545-552Google Scholar
  23. 23.
    Terlemez, O., Ulbrich, S., Mandery, C., Do, M., Vahrenkamp, N., Asfour, T.: Master Motor Map (MMM)—Framework and toolkit for capturing, representing, and reproducing human motion on humanoid robots. In: 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2014, pp. 894-901Google Scholar
  24. 24.
    Koenemann, J., Burget, F., Bennewitz, M.: Real-time imitation of human whole-body motions by humanoids. In: IEEE international conference on robotics and automation (ICRA) 2014, pp. 2806-2812Google Scholar
  25. 25.
    Nakaoka, S., Nakazawa, A., Yokoi, K., Hirukawa, H., Ikeuchi, K.: Generating whole body motions for a biped humanoid robot from captured human dances. In: IEEE international conference on robotics and automation (ICRA’03) 2003, pp. 3905-3910Google Scholar
  26. 26.
    Yamane, K., Hodgins, J.: Simultaneous tracking and balancing of humanoid robots for imitating human motion capture data. In: IEEE/RSJ international conference on intelligent robots and systems (IROS) 2009, pp. 2510-2517Google Scholar
  27. 27.
    Sakka, S., Poubel, L.P., Cehajic, D.: Tasks prioritization for whole-body realtime imitation of human motion by humanoid robots. In: Digital Intelligence (DI2014), September 2014, pp. 1-5Google Scholar
  28. 28.
    Kim, C., Kim, D., Oh, Y.: Adaptation of human motion capture data to humanoid robots for motion imitation using optimization. Integrated computer-aided engineering 13(4), 377–389 (2006)Google Scholar
  29. 29.
    Tosun, T., Mead, R., Stengel, R.: A general method for kinematic retargeting: adapting poses between humans and robots. In: ASME 2014 international mechanical engineering congress and exposition 2014, pp. V04AT04A027-V004AT004A027Google Scholar
  30. 30.
  31. 31.
    De Leva, P.: Adjustments to Zatsiorsky-Seluyanov’s segment inertia parameters. J. Biomech. 29(9), 1223–1230 (1996)CrossRefGoogle Scholar
  32. 32.
  33. 33.
    Kim, S., Shukla, A., Billard, A.: Catching objects in flight. IEEE Trans. Robot. 30(5), 1049–1065 (2014)CrossRefGoogle Scholar
  34. 34.
    Guan, Y., Yokoi, K.: Reachable space generation of a humanoid robot using the monte carlo method. In: IEEE/RSJ international conference on intelligent robots and systems 2006, pp. 1984-1989Google Scholar
  35. 35.
    Zacharias, F., Borst, C., Hirzinger, G.: Capturing robot workspace structure: representing robot capabilities. In: IEEE/RSJ international conference on intelligent robots and systems (IROS) 2007, pp. 3229-3236Google Scholar
  36. 36.
    Kofinas, N., Orfanoudakis, E., Lagoudakis, M.G.: Complete analytical forward and inverse kinematics for the NAO humanoid robot. J. Intell. Robot. Syst. 77(2), 251–264 (2015)CrossRefGoogle Scholar
  37. 37.
  38. 38.
    Jazar, R.N.: Theory of applied robotics: kinematics, dynamics, and control. Springer Science and Business Media (2010)Google Scholar
  39. 39.
    Sciavicco, L., Siciliano, B., Villani, L., Oriolo, G.: Robotics: modelling, planning and control. In: Springer London, 2009Google Scholar
  40. 40.

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Mina Alibeigi
    • 1
    Email author
  • Sadegh Rabiee
    • 1
  • Majid Nili Ahmadabadi
    • 2
    • 3
  1. 1.Cognitive Systems Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
  2. 2.Cognitive System Laboratory, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
  3. 3.School of Cognitive SciencesInstitute for Research in Fundamental Sciences (IPM)TehranIran

Personalised recommendations