Dynamic Control for Human-Humanoid Interaction

  • S. G. KhanEmail author
  • S. Bendoukha
  • M. N. Mahyuddin
Reference work entry


Achieving the current vision of future humanoid robots living with humans and assisting them in their daily tasks relies heavily on the development of safe and meaningful interaction between humanoids and humans. This chapter highlights and discusses dynamic control techniques for human–humanoid interaction (HHI), also referred to as human–robot interaction (HRI). Choosing the right control strategy is an essential part of HHI design. Some of the commonly used techniques include, for instance, balancing in mobile robots, trajectory generation, compliance or force control, etc. A description of the most relevant aspects of HHI control is given in this chapter with focus on the main challenges of the developers. Among the large number of techniques discussed here, specific attention is paid to force/compliance control and cooperative control due to their high potential and impressive results. An example of a leader–follower approach for HHI is presented at the end of the chapter.


  1. 1.
    R. Alami, A. Albu-Schaefer, A. Bicchi, R. Bischoff, R. Chatilla, D.A. Luca, D. Santis, G. Giralt, J. Jeremie Guiochet, G. Hirzinger, Safe and dependable physical human-robot interaction in anthropic domains: state of the art and challenges, in IROS, 2006Google Scholar
  2. 2.
    A. Albu-Schaffer, A.G. Bicchi, Boccadamo, R. Chatila, A.D. Luca, A.D. Santis, G. Giralt, G. Hirzinger, V. Lippiello, R. Mattone, R. Schiavi, B. Siciliano, G. Tonietti, L. Villan, Physical human-robot interaction in anthropic domains: safety and dependability (2005).
  3. 3.
    A. Albu-Schäffer, A. Bicchi, Actuators for soft robotics, in Springer Handbook of Robotics, ed. by B. Siciliano, O. Khatib (Springer, Berlin, 2016), pp. 499–530CrossRefGoogle Scholar
  4. 4.
    Alderban Robotics, Date Accesssed 6 July 2017
  5. 5.
    E.A. Al-Gallaf, Neurofuzzy inverse Jacobian mapping for multi-finger robot hand control. J. Intell. Robot. Syst. 39, 17–42 (2004)CrossRefGoogle Scholar
  6. 6.
    B. Alquadi, H. Modares, I. Ranatunga, S.M. Tousif, F.L. Lewis, D.O. Popa, Model reference adaptive impedance control for physical human-robot interaction. Control Theory Tech. 14(1), 68–82 (2016)Google Scholar
  7. 7.
    D. Araiza-Illan, K. Eder, Safe and trustworthy human robot interaction – physical safety, in Humanoid Robotics: A Reference, ed. by A. Goswami, P. Vadakkepat (Springer, 2018)Google Scholar
  8. 8.
    Y. Bae, S. Jung, Cartesian trajectory control of humanoid robot arms based on a disturbance observer, in 12th International Conference on Control, Automation and Systems, ICC, Jeju Island, 17–21 Oct 2012 (2012), pp. 947–951Google Scholar
  9. 9.
    Y.G. Bae, S. Jung, Experimental studies of position control of humanoid robot arms using a simplified time-delayed controller: a practical approach. in IEEE International Conference on Mechatronics and Automation, Takamatsu (2013), pp. 1399–1404Google Scholar
  10. 10.
    J. Baltes, S. McGrath, J. Anderson, The use of gyroscope feedback in the control of the walking gaits for a small humanoid robot, in RoboCup 2004, ed. by D. Nardi et al. LNAI, vol. 3276 (Springer, Berlin/New York, 2005), pp. 628–635CrossRefGoogle Scholar
  11. 11.
    A. Beck, L. Caamero, A. Hiolle, L. Damiano, P. Cosi, F. Tesser, G.a Sommavilla, Interpretation of emotional body language displayed by a humanoid robot: a case study with children. Int. J. Soc. Robot. 5, 325–334 (2013)CrossRefGoogle Scholar
  12. 12.
    K. Berns, T. Braun, A humanoid head for assistance robots, in Proceedings of the 8th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR (2005)Google Scholar
  13. 13.
    D. Bertram, J. Kuffner, R. Dillmann, T. Asfour, An integrated approach to inverse kinematics and path planning for redundant manipulators, in IEEE International Conference on Robotics and Automation (2006), pp. 1874–1879Google Scholar
  14. 14.
    A. Bicchi, V. Kumar, Robotic grasping and contact: a review, in Proceedings of ICRA (2000), pp. 348–353Google Scholar
  15. 15.
    D. Braun, F. Petit, F. Huber, S. Haddadin, P. van der Smagt, A. Albu-Scha̋ffer, S. Vijayakumar, Robots driven by compliant actuators: optimal control under actuation constraints. IEEE Trans. Robot. 29, 1085–1101 (2013)CrossRefGoogle Scholar
  16. 16.
    C. Breazeal, Emotion and sociable humanoid robots. Int. J. Hum.-Comput. Stud. 59(1–2), 119–155 (2003)CrossRefGoogle Scholar
  17. 17.
    J. Buchli, E. Theodorou, F. Stulp, S. Schaal, Variable impedance control – a reinforcement learning approach. in Proceedings of Robotics: Science and Systems, Zaragoza (2010)Google Scholar
  18. 18.
    I.M. Bullock, J.Z. Zheng, S.D.L. Rosa, C. Guertler, A.M. Dollar, Grasp frequency and usage in daily household and machine shop tasks. IEEE Trans. Haptics 6(3), 296–308 (2013)CrossRefGoogle Scholar
  19. 19.
    S.R. Buss, Introduction to inverse kinematics with Jacobian transpose, pseudoinverse and damped least squares methods. IEEE J. Robot. Autom. 17(1), 1–19 (2004)Google Scholar
  20. 20.
    G. Chen, F.-L. Lewis, Distributed adaptive controller design for the unknown networked Lagrangian systems, in 2010 49th IEEE Conference on Decision and Control (CDC) (2008), pp. 6698–6703Google Scholar
  21. 21.
    X. Chen, S. Ke, D. Wei, H. Xu, L. Wang, B. Wang, Research and development of the head unit of interactive humanoid robot, in Recent Advances in CSIE (2011), pp. 601–606Google Scholar
  22. 22.
    L. Cheng, Z.-G. Hou, M. Tan, Decentralized adaptive consensus control for multi-manipulator system with uncertain dynamics, in 2008 IEEE International Conference on Systems, Man and Cybernetics, Oct 2008, pp. 2712–2717Google Scholar
  23. 23.
    L. Colasanto, N.V. Noot, A.J. Ijspeert, Bio-inspired walking for humanoid robots using feet with human-like compliance and neuromuscular control, in IEEE-RAS International Conference on Humanoid Robots (2015)Google Scholar
  24. 24.
    R. Colbaugh, K. Glass, K. Wedeward, Adaptive compliance control of electrically-driven manipulators, in Proceedings of the 35th Conference on Decision and Control, Kobe (1996), pp. 394–399Google Scholar
  25. 25.
    Cooperative Human Robot Interaction System (CHRIS) FP7 EU Funded Project 2008–2012, Date Accessed 26 Oct 2016
  26. 26.
    J.J. Craig, Introduction to Robotics: Mechanics and Control, vol. 3 (Pearson Prentice Hall, Upper Saddle River, 2005)Google Scholar
  27. 27.
    A. Curioni, G. Knoblich, N. Sebanz, Joint action in humans – a model for human-robot interactions? in Humanoid Robotics: A Reference, ed. by A. Goswami, P. Vadakkepat (Springer, 2018)Google Scholar
  28. 28.
    E. Demircan, T. Besier, S. Menon, O. Khatib, Human motion reconstruction and synthesis of human skills, in Advances in Robot Kinematics: Motion in Man and Machine (Springer, New York, 2010), pp. 283–292CrossRefGoogle Scholar
  29. 29.
    P. Ekman, W. Friesen, Facial Action Coding System (Consulting psychologist Press Inc., Palo Alto, 1978)Google Scholar
  30. 30.
    A. Eskande, N. Mansard, P.B. Wieber, Fast resolution of hierarchized inverse kinematics with inequality constraints, in IEEE International Conference on Robotics and Automation (2010), pp. 3733–3738Google Scholar
  31. 31.
    E. Falotico, N. Cauli, P. Kryczka, K. Hashimoto, A. Berthoz, A. Takanishi, P. Dario, C. Laschi, Head stabilization in a humanoid robot: models and implementations. Auton. Robot. 41(2), 349–365 (2017)CrossRefGoogle Scholar
  32. 32.
    I. Farkhatdinov, V. Hayward, A. Berthoz, On the benefits of head stabilization with a view to control balance and locomotion in humanoids, in IEEE-RAS International Conference on Humanoid Robots (2011), pp. 147–152Google Scholar
  33. 33.
    T. Feix, J. Romero, H. Schmiedmayer, A.M. Dollar, D. Kragic, The GRASP taxonomy of human grasp types. IEEE Trans. Hum. Mach. Syst. 46(1), 66–77 (2016)CrossRefGoogle Scholar
  34. 34.
    T. Fong, I. Nourbakhsh, K. Dautenhahn, A survey of socially interactive robots. Robot. Autonom. Syst. 42, 143–166 (2003)CrossRefGoogle Scholar
  35. 35.
    M. Fumagalli, S. Ivaldi, M. Randazzo, L. Natale, G. Metta, G. Sandini, Force feedback exploiting tactile and proximal force/torque sensing. Theory and implementation on the humanoid robot iCub. Autonomous RobotsGoogle Scholar
  36. 36.
    M. Fumagalli, L. Jamone, G. Metta, L. Natale, F. Nori, A. Parmiggiani, M. Randazzo, G. Sandini, A force sensor for the control of a human-like tendon driven neck, in 9th IEEE-RAS International Conference on Humanoid Robots, Paris, 7–10 Dec 2009, pp. 478–485Google Scholar
  37. 37.
    M. Fumagalli, A. Gijsberts, S. Ivaldi, L. Jamone, G. Metta, L. Natale, F. Nori, G. Sandini, Learning to exploit proximal force sensing: a comparison approach, in From Motor Learning to Interaction Learning in Robots, ed. by O. Sigaud, J. Peters. Studies in Computational Intelligence, vol. 264 (Springer, Berlin/Heidelberg, 2010), pp. 159–177Google Scholar
  38. 38.
    B. Gao, J. Xu, J. Zhao, N. Xi, Y. Shen, R. Yang, A humanoid neck system featuring low motion-noise. J. Intell. Robot. Syst. 67, 101–116 (2012)CrossRefGoogle Scholar
  39. 39.
    S.S. Ge, Y. Li, H. He, Neural-network-based human intention estimation for physical human-robot interaction, in 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) (2011), pp. 390–395Google Scholar
  40. 40.
    S. Haddadin, E. Croft, Physical human–robot interaction, in Springer Handbook of Robotics, ed. by B. Siciliano, O. Khatib (Springer, Berlin, 2016), pp. 1835–1874CrossRefGoogle Scholar
  41. 41.
    A. Hemami, Kinematics of two-arm robots. IEEE J. Robot. Autom. 2(4), 225–228 (1986)CrossRefGoogle Scholar
  42. 42.
    D. Hemmi, G. Herrmann, J. Na, M.N. Mahyuddin, Adaptive optimal tracking control applied for a humanoid robot arm, in IEEE International Symposium on Intelligent Control (ISIC) (2015)Google Scholar
  43. 43.
    G. Herrmann, J. Jalani, M.N. Mahyuddin, S.G. Khan, C. Melhuish, Robotic hand posture and compliant grasping control using operational space and integral sliding mode control. Robotica 34(10), 2163–2185 (2016)CrossRefGoogle Scholar
  44. 44.
    C.H. Hjorztsj, Man’s Face and Mimic Language (Studen litteratur, Sweden, 1969)Google Scholar
  45. 45.
    Y. Hong, J. Hu, L. Gao, Tracking control for multi-agent consensus with an active leader and variable topology. Automatica 42(7), 1177–1182 (2006)MathSciNetCrossRefGoogle Scholar
  46. 46.
    F. Hu, X. Wu, D. Luo, Learning basic unit movements for humanoid arm motion control, in IEEE Advanced Motion Control (2016)Google Scholar
  47. 47.
    C.M. Hubicki, A. Hereid, M.X. Grey, A.L. Thomaz, A.D. Ames, Work those arms: toward dynamic and stable humanoid walking that optimizes full-body motion, in IEEE International Conference on Robotics and Automation (ICRA) (2016)Google Scholar
  48. 48.
    A. Ibanez, P. Bidaud, V. Padois, Emergence of humanoid walking behaviors from mixed-integer model predictive control, in IEEE/RSJ International Conference on Intelligent Robots and Systems (2014)Google Scholar
  49. 49.
    S. Ivaldi, M. Fumagalli, M. Randazzo, F. Nori, G. Metta, G. Sandini, Computing robot internal/external wrenches by means of F/T sensors: theory and implementation on the iCub humanoid, in Robotics Science and Systems (2011)Google Scholar
  50. 50.
    L. Jamone, M. Fumagalli, L. Natale, F. Nori, G. Metta, G. Sandini, Machine-learning based control of a human-like tendon-driven neck, in IEEE International Conference on Robotics and Automation, Anchorage, 3–8 May 2010Google Scholar
  51. 51.
    M. Jantsch, S. Wittmeier, K. Dalamagkidis, G. Hermann, A. Knoll, Adaptive neural network dynamic surface control: an evaluation on the musculoskeletal robot anthrob, in Proceedings of IEEE International Conference on Robotics and Automation ICRA (2015), pp. 4347–4352Google Scholar
  52. 52.
    S. Jean Jacques, Adaptive manipulator control: a case study. IEEE Trans. Autom. Control 33(11), 995–1003 (1988)Google Scholar
  53. 53.
    J.-H. Jean, L.C. Fu, An adaptive control scheme for coordinated multimanipulator systems. IEEE Trans. Robot. Autom. 9(2), 226–231 (1993)CrossRefGoogle Scholar
  54. 54.
    A. Jevtic, E. Lucet, A. Koslov, J. Gancet, Intro: a multidisciplinary approach to intelligent human-robot interaction, in World Automation Congress (WAC), June 2012, pp. 1–6Google Scholar
  55. 55.
    M. Jones, J. Rehg, Statistical color models with application to skin detection, in IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (1999), pp. 274–280Google Scholar
  56. 56.
    T. Kanda, T. Miyashita, Y. Haikawa, H. Ishiguro, Analysis of humanoid appearances in human robot interaction. IEEE Tran. Robot. 24(3), 725 (2008)CrossRefGoogle Scholar
  57. 57.
    N. Kashiri, G.A. Medrano-Cerda, N.G. Tsagarakis, M. Laffranchi, D. Caldwell, Damping control of variable damping compliant actuators, in 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle (2015), pp. 850–856Google Scholar
  58. 58.
    N. Kashiri, M. Laffranchi, D.G. Caldwell, N.G. Tsagarakis, Dynamics and control of an anthropomorphic compliant arm equipped with friction Clutches. IEEE/ASME Trans. Mechatron. 21(2), 694–707 (2016)CrossRefGoogle Scholar
  59. 59.
    S.G. Khan, G. Herrmann, T. Pipe, C. Melhuish, Adaptive multi-dimensional compliance control of a humanoid robotic arm with anti-windup compensation, in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010), pp. 2218–2223Google Scholar
  60. 60.
    S.G. Khan, G. Herrmann, T. Pipe, C. Melhuish, A. Spiers, Safe adaptive compliance control of a humanoid robotic arm with anti-windup compensation and posture control. Int. J. Soc. Robot. 2, 305–319 (2010)CrossRefGoogle Scholar
  61. 61.
    S.G. Khan, L. Alexander, H. Guido, P. Tony, M. Chris, Toward safe human robot interaction: integration of compliance control, an anthropomorphic hand and verbal communication, in Proceedings of the 2011 Next Wave in Robotics: 14th FIRA RoboWorld Congress, FIRA 2011, Kaohsiung, ed. by T.-H.S. Li, K.-Y. Tu, C.-C. Tsai, C.-C. Hsu, C.-C. Tseng, P. Vadakkepat, J. Baltes, J. Anderson, C.-C. Wong, N. Jesse, C.-H. Kuo, H.-C. Yang, 26–30 Aug 2011 (Springer, Berlin/Heidelberg), pp. 17–24.Google Scholar
  62. 62.
    S.G. Khan, G. Herrmann, F.L. Lewis, T. Pipe, C. Melhuish, A Q-learning based Cartesian model reference compliance controller implementation for a humanoid robot arm, in IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM) (2011), pp. 214–219Google Scholar
  63. 63.
    S.G. Khan, Adaptive and reinforcement learning control methods for active compliance control of a humanoid robot arm, PhD Thesis (2012)Google Scholar
  64. 64.
    S.G. Khan, G. Herrmann, F.L. Lewis, A.G. Pipe, C. Melhuish, Reinforcement learning and optimal adaptive control: an overview and implementation examples. Annu. Rev. Control. 36(1), 42–59 (2012)CrossRefGoogle Scholar
  65. 65.
    S.G. Khan, G. Herrmann, M. Al-Grafi, T. Pipe, C. Melhuish, Compliance control and human robot interaction Part I survey. Int. J. Humanoid Rob. 11, 1430 001–1430 001–28 (2014)Google Scholar
  66. 66.
    S.G. Khan, G. Herrmann, A. Lenz, M. Al-Grafi, T. Pipe, C. Melhuish, Compliance control and human robot interaction Part II experimental examples. Int. J. Humanoid Rob. 11, 1430 002–1–430 002–21 (2014)CrossRefGoogle Scholar
  67. 67.
    S.G. Khan, J. Jalani, Realisation of model reference compliance control of a humanoid robot arm via integral sliding mode control. J. Mech. Sci. 7, 1–8 (2016)CrossRefGoogle Scholar
  68. 68.
    O. Khatib, J. Burdick, Motion and force control of robot manipulators, in Proceedings of 1986 IEEE International Conference on Robotics and Automation (1986), pp. 1381–1386Google Scholar
  69. 69.
    O. Khatib, J. Burdick, Force control of robot manipulators. in Proceedings of 1986 IEEE International Conference on Robotics and Automation, vol. 3 (1987)Google Scholar
  70. 70.
    S. Kjaita, F. Kanehiro, K. Kaneko, K. Yokoi, H. Hirukawa, The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation, in IEEE/RSJ International Conference on Intelligent Robots and Systems (2001), pp. 239–246Google Scholar
  71. 71.
    A.-J. Koivo, Adaptive position-velocity-force control of two manipulators, in 1985 24th IEEE Conference on Decision and Control, vol. 4 (IEEE, Dec 1985), pp. 1529–1532Google Scholar
  72. 72.
    C. Kuan, K. Young, Reinforcement learning and robust control for robot compliance tasks. J. Intell. Robot. Syst. 23(2), 165–182 (1998)Google Scholar
  73. 73.
    F. Lacquaniti, J.-F. Soechting, Coordination of arm and wrist motion during a reaching task. J. Neurosci. 2(4), 399–408 (1982)CrossRefGoogle Scholar
  74. 74.
    M. Laffranchi, L. Chen, N. Kashiri, J. Lee, N.G. Tsagarakis, D.G. Caldwell, Development and control of a series elastic actuator equipped with a semi active friction damper for human friendly robots. Robot. Auton. Syst. 62(12), 1827–1836 (2014)CrossRefGoogle Scholar
  75. 75.
    J. Law, P. Shaw, M. Lee, A biologically constrained architecture for developmental learning of eye–head gaze control on a humanoid robot. Auton. Robot. 35, 77–92 (2013)CrossRefGoogle Scholar
  76. 76.
    S. Lee, S. Baek, J. Kim, Arm trajectory generation based on RRT* for humanoid robot, in Robot Intelligence Technology and Applications 3, ed. by J.-H. Kim et al. Advances in Intelligent Systems and Computing, vol. 345 (Springer, Cham, 2015)Google Scholar
  77. 77.
    Y.F. Lee, C.Y. Chu, J.Y. Xu, C.C. Lan, A humanoid robotic wrist with two-dimensional series elastic actuation for accurate force/torque interaction. IEEE/ASME Trans. Mechatron. 21(3), 1315–1325 (2016)CrossRefGoogle Scholar
  78. 78.
    S. Levine, C. Finn, T. Darrell, P. Abbeel, End-to-end training of deep visuomotor policies (2015). arXiv preprint arXiv:1504.00702Google Scholar
  79. 79.
    F.L. Lewis, D. Dawson, C. Abdallah, Robot Manipulator Control: Theory and Practice (Marcel Dekker Inc., New York, 2003)Google Scholar
  80. 80.
    F.L. Lewis, D.M. Dawson, C.T. Abdallah, Robot Manipulator Control; Theory and Practice, 2nd edn. (Marcel Dekker, New York/Basel, 2004)Google Scholar
  81. 81.
    Q. Li, L. Lihuan, L. Fucai, J. Zhenlin, The application of adaptive backstepping sliding mode for hybrid humanoid robot arm trajectory tracking control, in Proceedings of the 2013 International Conference on Advanced Mechatronic Systems (2013)Google Scholar
  82. 82.
    Y.-H. Liu, S. Arimoto, V.-P. Vega, K. Kitagaki, Decentralized adaptive control of multiple manipulators in co-operations. Int. J. Control. 67(5), 649–674 (1997)MathSciNetCrossRefGoogle Scholar
  83. 83.
    G. Liu, F. Zha, M. Li, W. Guo, P. Wang, H. Cai, Control of a humanoid robot walking with dynamic balance, in IEEE International Conference on Robotics and Biomimetics (ROBIO) (2013)Google Scholar
  84. 84.
    L. Liu, G. Liu, Y. Zhang, Grasping control in three-fingered robot hand teleoperation using desktop haptic device, in EuroHaptics 2014, Part II, ed. by M. Auvray, C. Duriez. LNCS, vol. 8619 (2014), pp. 232–240Google Scholar
  85. 85.
    S. Lo, H. Huang, Realization of sign language motion using a dual-arm/hand humanoid robot. Intel. Serv. Robot. 9, 333–345 (2016)CrossRefGoogle Scholar
  86. 86.
    K.S. Lohan, H. Lehmann, C. Dondrup, F. Broz, H. Kose, Enriching the human-robot interaction loop with natural, semantic and symbolic gestures, in Humanoid Robotics: A Reference, ed. by A. Goswami, P. Vadakkepat (Springer, 2018)Google Scholar
  87. 87.
    R.C. Luo, Y. Perng, B. Shih, Y. Tsai, Cartesian position and force control with adaptive impedance/compliance capabilities for a humanoid robot arm, in IEEE International Conference on Robotics and Automation (ICRA) (2013)Google Scholar
  88. 88.
    R.R. Ma, A. Spiers, A.M. Dollar, Gripper: extending the dexterity of a simple, underactuated gripper, in Advances in Reconfigurable Mechanisms and Robots II. ed. by X. Ding, X. Kong, J.S. Dai. Series Mechanisms and Machine Science, vol. 36 (Springer, London, 2015), pp. 795–805Google Scholar
  89. 89.
    M.N. Mahyuddin, A novel adaptive algorithm and its application to estimation and distributed control. PhD thesis, University of Bristol (2014)Google Scholar
  90. 90.
    M.N. Mahyuddin, G. Herrmann, Cooperative robot manipulator control with human ‘pinning’ for robot assistive task execution, in Social Robotics, ed. by G. Herrmann, M.J. Pearson, A. Lenz, P. Bremner, A. Spiers, U. Leonards. Lecture Notes in Computer Science, vol. 8239 (Springer, Berlin, 2013), pp. 521–530CrossRefGoogle Scholar
  91. 91.
    M.N. Mahyuddin, G. Herrmann, Distributed motion synchronisation control of humanoid arms, in 5th International Conference on Advance Humanoid Robotic Research, FIRA RoboWorld Congress 2013 (Springer, Berlin/Heidelberg, 2013)CrossRefGoogle Scholar
  92. 92.
    M.N. Mahyuddin, G. Herrmann, S.G. Khan, A novel adaptive control algorithm in application to a humanoid robot arm, in Advances in Autonomous Robotics, ed. by G. Herrmann, M. Studley, M. Pearson, A. Conn, C. Melhuish, M. Witkowski, J.-H. Kim, P. Vadakkepat. Lecture Notes in Computer Science, vol. 7429 (Springer, Berlin/Heidelberg, 2012), pp. 25–36CrossRefGoogle Scholar
  93. 93.
    M.N. Mahyuddin, S.G. Khan, G. Herrmann, A novel robust adaptive control algorithm with finite-time online parameter estimation of a humanoid robot arm. Robot. Auton. Syst. 62, 294–305 (2014)CrossRefGoogle Scholar
  94. 94.
    N. Mansard, O. Stasse, P. Evrard, A. Kheddar, A versatile general inverted kinematics implementation for collaborative working humanoid robots: the stack of tasks, in IEEE International Conference on Advanced Robotics (2009) pp. 1–9Google Scholar
  95. 95.
    Z. Miljkovic, M. Mitic, M. Lazarevic, B. Babi, Neural network reinforcement learning for visual control of robot manipulators. Expert Syst. Appl. 40(5), 1721–1736 (2013)CrossRefGoogle Scholar
  96. 96.
    B. Miller, D. Feil-Seifer, Embodiment, situatedness and morphology for humanoid interaction, in Humanoid Robotics: A Reference, ed. by A. Goswami, P. Vadakkepat (Springer, 2018)Google Scholar
  97. 97.
    Z. Mohamed, M. Kitani, S. Kaneko, G. Capi, Humanoid robot arm performance optimization using multi objective evolutionary algorithm. Int. J. Control Autom. Syst. 12(4), 870–877 (2014)CrossRefGoogle Scholar
  98. 98.
    N. Morita, H. Nogami, Y. Hayashida, E. Higurashi, T. Ito, R. Sawada, Development of a miniaturized laser doppler velocimeter for use as a slip sensor for robot hand control, in MEMS 2015, pp. 18–22Google Scholar
  99. 99.
    T. Naniwa, S. Arimoto, V.-P. Vega, A model-based adaptive control scheme for coordinated control of multiple manipulators, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’94) (IEEE), pp. 695–702Google Scholar
  100. 100.
    T.D. Nguyen, T.T. Nguyen, T.P. Tran, Design and control humanoid hand to implement hand shaking task applied for service robot, in AETA 2015: Recent Advances in Electrical Engineering and Related Sciences. Series Lecture Notes in Electrical Engineering, vol. 371 (Springer, Cham, 2016), pp. 745–756CrossRefGoogle Scholar
  101. 101.
    K. Noda, H. Arie, Y. Suga, T. Ogata, Multimodal integration learning of object manipulation behaviors using deep neural networks, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2013), pp. 1728–1733Google Scholar
  102. 102.
    T. Nomura, Empathy as signaling feedback between humanoid robots and humans, in Humanoid Robotics: A Reference, ed. by A. Goswami, P. Vadakkepat (Springer, 2018)Google Scholar
  103. 103.
    NRCG HRI Compliance Control, Accessed Date 09 July 2017
  104. 104.
    E. Oztop, D.W. Franklin, T. Chaminde, Human–humanoid interaction: is a humanoid robot perceived as a human? Int. J. Humanoid Robot. 2(4), 537–559 (2005)Google Scholar
  105. 105.
    Physical Human Robot Interaction Depenability and Safety (PHRIENDS), Accessed 26 Oct 2016
  106. 106.
    Robotics and Mechatronics Centre, German Aerospace Centre-DLR.
  107. 107.
    S. Romdhani, P. Torr, B. Scholkopf, A. Blake, Computationally efficient face detection, in Proceedings of the 8th International Conference on Computer Vision, vol. 2 (2001), pp. 695–700Google Scholar
  108. 108.
    J.-F. Rosenblith, In the Beginning: Development Fromconception to Age Two, 2nd edn. (Sage Publications, Newbury Park, 1992)Google Scholar
  109. 109.
    G. Sandini, A. Sciutti, F. Rea, Movement-based communication for humanoid-human interaction, in Humanoid Robotics: A Reference, ed. by A. Goswami, P. Vadakkepat (Springer, 2018)Google Scholar
  110. 110.
    A.D. Santis, Modelling and control for human-robot interaction. PhD thesis, Universita. Degli Studi Di Napoli Federico II, Dottorato di Ricerca in Ingegneria Informatica ed Automatica (2007)Google Scholar
  111. 111.
    R.-E. Schapire, Y. Singer, Improved boosting algorithms using confidence-rated predictions, in Proceedings of the Eleventh Annual Conference on Computational Learning Theory (1998), pp. 80–91Google Scholar
  112. 112.
    C. Semini, V. Barasuol, T. Boaventura, M. Frigerio, J. Buchli, Is active impedance the key to a breakthrough for legged robots?, in Robotics Research: The 16th International Symposium ISRR, 2016, ed. by M. Inaba, P. Corke (Springer International Publishing), pp. 3–8Google Scholar
  113. 113.
    N. Shafii, N.O. Lau, L. Reis, Learning to walk fast: optimized hip height movement for simulated and real humanoid robots. J. Intell. Robot. Syst. 80, 555–571 (2015)CrossRefGoogle Scholar
  114. 114.
    J. Shi, H. Liu, N. Bajcinca, Robust control of robotic manipulators based on integral sliding mode. Int. J. Control 81, 1537–1548 (2008)MathSciNetCrossRefGoogle Scholar
  115. 115.
    S. Shirafuji, K. Hosoda, Detection and prevention of slip using sensors with different properties embedded in elastic artificial skin on the basis of previous experience. Robot. Auton. Syst. 62, 46–52 (2014)Google Scholar
  116. 116.
    B. Siciliano, L. Villani, Robot Force Control (Springer, Boston, 1999)CrossRefGoogle Scholar
  117. 117.
    A. Spiers, G. Herrmann, C. Melhuish, T. Pipe, A. Lenz, Robotic implementation of realistic reaching motion using a sliding mode/operational space controller, in Advances in Robotics – Proceedings of First ICSR 2009 (Springer, 2009), pp. 230–238Google Scholar
  118. 118.
    A. Takanishi, H. Miwa, H. Takanobu, Development of human-like head robots for modeling human mind and emotional humanrobot interaction, in IARP International Workshop on Humanoid and Human Friendly Robotics (2002), pp. 104–109Google Scholar
  119. 119.
    F. Tamar, N. Hogan, The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5(7), 1688–1703 (1985)CrossRefGoogle Scholar
  120. 120.
    F. Tavakoli, V. Derhami, A. Kamalinejad, Control of humanoid robot walking by fuzzy sarsa learning, in RSI International Conference on Robotics and Mechatronics (2015)Google Scholar
  121. 121.
    S. Teshigawara, Highly sensitive sensor for detection of initial slip and its application in a multi-fingered robot hand, in IEEE International Conference on Robotics and Automation (2011), pp. 1097–1102Google Scholar
  122. 122.
    M. Uchiyama, P. Dauchez, Hybrid position/force control for coordination of a two-arm robot, in Proceedings of the IEEE International Conference on Robotics and Automation, Raleigh (1987), number c, pp. 1242–1247Google Scholar
  123. 123.
    M. Vukobratovic, B. Borovac, K. Babkovic, Contribution to the study of anthropomorphism of humanoid roboits. Int. J. Humanoid Rob. 2(3), 361–387 (2005)CrossRefGoogle Scholar
  124. 124.
    M.H. Wu, S. Ogawa, A. Konno, Symmetry position/force hybrid control for cooperative object transportation using multiple humanoid robots. Adv. Robot. 30(2), 131–149 (2009)CrossRefGoogle Scholar
  125. 125.
    J. Yang, D. Zhang, A.F. Frangi, J. Yang, Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–7 (2004)Google Scholar
  126. 126.
    T. Yoshikawa, T. Sugie, M. Tanaka, Dynamic hybrid position/force control of robot manipulators–controller design and experiment, in Proceedings of 1987 IEEE International Conference on Robotics and Automation (1987), pp. 2005–2010Google Scholar
  127. 127.
    X. Yulin, J. Caijun, Y. jie, in Compliance Control for Grasping with a Bionic Robot Hand, Chinese Control and Decision Conference (CCDC) (2016), pp. 5280–5285Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, College of Engineering YanbuTaibah University, Yanbu BranchYanbuSaudi Arabia
  2. 2.Department of Electrical Engineering, College of Engineering YanbuTaibah University, Yanbu BranchYanbuSaudi Arabia
  3. 3.School of Electrical and Electronics EngineeringUniversiti Sains MalaysiaNibong TebalMalaysia

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