A Comparative Study Between Humans and Humanoid Robots

  • Katsu YamaneEmail author
  • Akihiko Murai
Reference work entry


Humanoid robots are usually designed with the goal to realize humanlike topology, structure, and physical properties, as it would allow the robots to work in existing infrastructure built for humans. For example, most humanoid robots have two arms and/or two legs, and each leg typically consists of a three-degrees-of-freedom (DOF) hip, a one-DOF knee, and two- or three-DOF ankle joints, similar to the human leg structure. Many humanoid robots have been successful in emulating the human body at least at a higher level. However, there are many important differences between existing humanoid robot hardware and the human body. For example, it is often impossible to physically implement the same kinematic properties such as number of joints and range of motion using commonly available materials and components. Actuators are also very different because none of the existing man-made actuators can match the power density of human muscles. Although understanding human motor control has seen a lot of progress and inspired some robot controllers lately, there are still significant differences in their complexity and performance. These differences between the human body and humanoid robots have significant impact on the physical capability of current humanoid robot hardware. Studying them may therefore give insights on how humanoid robotics researchers can improve the agility and versatility of humanoid robots.


  1. 1.
    Y. Nakamura, K. Yamane, Y. Fujita, I. Suzuki, Somatosensory computation for man-machine interface from motion capture data and musculoskeletal human model. IEEE Trans. Robot. 21(1), 58–66 (2005)CrossRefGoogle Scholar
  2. 2.
    K. Kaneko, F. Kanehiro, S. Kajita, H. Hirukawa, T. Kawasaki, M. Hirata, K. Akachi, M. Isozumi, Humanoid Robot HRP-2, in Proceedings of IEEE International Conference on Robotics and Automation, 2004, pp. 1083–1090Google Scholar
  3. 3.
    P.A. Bhounsule, K. Yamane, Iterative learning control for accurate task-space tracking with humanoid robots, in International Conference on Humanoid Robots, 2015, pp. 490–496Google Scholar
  4. 4.
    T. Kurz, Stretching Scientifically: A Guide to Flexibility Training (Stadion, Island Pond, 1994)Google Scholar
  5. 5.
    K. Nishiwaki, T. Sugihara, S. Kagami, F. Kanehiro, M. Inaba, H. Inoue, Design and development of research platform for perception-action integration, in humanoid robot: H6, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2000, pp. 1559–1564Google Scholar
  6. 6.
    D. Gouaillier, P. Hugel, V. Blazevic, C. Kilner, J. Monceaux, P. Lafourcade, B. Marnier, J. Serre, B. Maisonnier, Mechatronic design of NAO humanoid, in Proceedings of IEEE International Conference on Robotics and Automation, 2009, pp. 769–774Google Scholar
  7. 7.
    Y. Ogura, K. Aikawa, K. Shimomura, H. Kondo, A. Morishima, H. Lim, A. Takanishi, Development of a new humanoid robot WABIAN-2, in Proceedings of IEEE International Conference on Robotics and Automation, 2006, pp. 76–81Google Scholar
  8. 8.
    J. Englsberger, A. Werner, C. Ott, B. Henze, M. Roa, G. Garofalo, R. Burger, A. Beyer, O. Eiberger, K. Schmid, A. Albu-Schäffer, Overview of the torque-controlled humanoid robot TORO, in Proceedings of IEEE-RAS International Conference on Humanoid Robots, 2014, pp. 916–923Google Scholar
  9. 9.
    I.W. Park, J.Y. Kim, J. Lee, J.H. Oh, Mechanical design of the humanoid robot platform, HUBO. Adv. Robot. 21(11), 1305–1322 (2007)CrossRefGoogle Scholar
  10. 10.
    H.F. Schulte, The characteristics of the McKibben artificial muscle. Appl. External Power Prosthet. Orthotics 874, 94–115 (1961)Google Scholar
  11. 11.
    C.S. Haines et al., Artificial muscles from fishing line and sewing thread. Science 343, 868–872 (2014)CrossRefGoogle Scholar
  12. 12.
    M. Yip, G. Niemeyer, High-performance robotic muscles from conductive nylon sewing thread, in Proceedings of IEEE International Conference on Robotics and Automation, 2015, pp. 2313–2318Google Scholar
  13. 13.
    J.H. Warfel, The Extremities: Muscles and Motor Points (Lea & Febiger, Philadelphia, 1974)Google Scholar
  14. 14.
    J.H. Warfel, The Head, Neck, and Trunk (Lea & Febiger, Phiradelphia, 1993)Google Scholar
  15. 15.
    Y. Nakanishi, Y. Asano, T. Kozuki, H. Mizoguchi, Y. Motegi, M. Osada, T. Shirai, J. Urata, K. Okada, M Inaba, Design concept of detail musculoskeletal humanoid “Kenshiro” – toward a real human body musculoskeletal simulator, in Proceedings of the 2012 IEEE-RAS International Conference on Humanoid Robots, 2012, pp. 1–6Google Scholar
  16. 16.
    J. Rasmussen, M. Damsgaard, M. Voigt, Muscle recruitment by the min/max criterion – a comparative study. J. Biomech. 34(3), 409–415 (2001)Google Scholar
  17. 17.
    K. Yamane, Y. Fujita, Y. Nakamura, Estimation of physically and physiologically valid somatosensory information, in Proceedings of IEEE International Conference on Robotics and Automation, Barcelona, 2005, pp. 2635–2641Google Scholar
  18. 18.
    A.V. Hill, The heat of shortening and the dynamic constants of muscle, B126, 136–195 (1938)Google Scholar
  19. 19.
    S. Stroeve, Impedance characteristics of a neuro-musculoskeletal model of the human arm I: posture control. J. Biol. Cybern. 81, 475–494 (1999)CrossRefGoogle Scholar
  20. 20.
    G. Tonietti, R. Schiavi, A. Bicchi, Design and control of a variable stiffness actuator for safe and fast physical human/robot interaction, in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 526–531Google Scholar
  21. 21.
    S. Wolf, G. Hirzinger, A new variable stiffness design: matching requirements of the next robot generation, in IEEE International Conference on Robotics and Automation, 2008, pp. 1741–1746Google Scholar
  22. 22.
    N.G. Tsagarakis, I. Sardellitti, D.G. Caldwell, A new variable stiffness actuator (CompAct-VSA): design and modelling, in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 378–383Google Scholar
  23. 23.
    S.K. Au, H.M. Herr, Powered ankle-foot prosthesis. Robot. Autom. Mag. IEEE 15(3), 52–59 (2008)Google Scholar
  24. 24.
    M.B. Wiggin, G.S. Sawicki, S.H. Collins, An exoskeleton using controlled energy storage and release to aid ankle propulsion, in IEEE International Conference on Rehabilitation Robotics, 2011, pp. 1–5Google Scholar
  25. 25.
    C.S. Sherrington, The Integrative Action of the Nervous System (Yale University Press, New Haven, 1906)CrossRefGoogle Scholar
  26. 26.
    A. Murai, K. Yamane, A neuromuscular locomotion controller that realizes human-like responses to unexpected disturbances, in Proceedings of the 33th IEEE International Conference on Robotics and Automation (ICRA2011), 2011, pp. 1997–2002Google Scholar
  27. 27.
    A. Murai, K Yamane, Y. Nakamura, Effects of nerve signal transmission delay in somatosensory reflex modeling based on inverse dynamics and optimization, in Proceedings of the 32th IEEE International Conference on Robotics and Automation (ICRA2010), 2010, pp. 5076–5083Google Scholar
  28. 28.
    A. Prochazka, M. Gorassini, Models of ensemble firing of muscle spindle afferents recorded during normal locomotion in cats. J. Physiol. 507, 277–291 (1998)CrossRefGoogle Scholar
  29. 29.
    M.P. Mileusnic, G.E. Loeb, Mathematical models of proprioceptors. II. Structure and function of the golgi tendon organ. J. Neurophysiol. 96, 1789–1802 (2006)CrossRefGoogle Scholar
  30. 30.
    C.D. Clemente, Gray’s Anatomy ed 30 (Lea & Febiger, Phyladellphia, 1985)Google Scholar
  31. 31.
    A.M.R. Agur, Grant’s Atlas of Anatomy (Williams & Wilkins, Baltimore, 1991)Google Scholar
  32. 32.
    A.E. Bryson, Y.-C. Ho, Applied Optimal Control (Blaisdell, New York, 1969)Google Scholar
  33. 33.
    G. Rizzolatti, L. Craighero, The mirror-neuron system. Annu. Rev. Neurosci. 27, 169–192 (2004)CrossRefGoogle Scholar
  34. 34.
    P.A. Guertin, The mammalian central pattern generator for locomotion. Brain Res. Rev. 62, 54–56 (2009)CrossRefGoogle Scholar
  35. 35.
    P.A. Guertin, Central pattern generator for locomotion: anatomical, physiological, and pathophysiological considerations. Front. Neurol. 3, Article 183 (2012)Google Scholar
  36. 36.
    F.C. Anderson, M.G. Pandy, Dynamic optimization of human walking. J. Biomech. Eng. 123, 381–390 (2001)CrossRefGoogle Scholar
  37. 37.
    J.M. Wang, S.R. Hamner, S.L. Delp, V. Koltun, Optimizing locomotion controllers using biologically-based actuators and objectives. ACM Trans. Robot. 31(4), Article 25 (2012)Google Scholar
  38. 38.
    K. Yamane, Y. Nakamura, Dynamics filter – concept and implementation of on-line motion generator for human figures. IEEE Trans. Robot. Autom. 19(3), 421–432 (2003)CrossRefGoogle Scholar
  39. 39.
    K. Miura, M. Morisawa, S. Nakaoka, F. Kanehiro, K. Harada, K. Kaneko, S. Kajia, Robot motion remix based on motion capture data – towards human-like locomotion of humanoid robots, in Proceedings of IEEE-RAS International Conference on Humanoid Robots, 2009, pp. 596–603Google Scholar
  40. 40.
    M. Vukobratovic, B. Borovac, Zero-moment point – thirty five years of its life. Int. J. Humanoid Robot. 1(1), 157–173 (2004)CrossRefGoogle Scholar
  41. 41.
    Q.C. Pham, Y. Nakamura, Time-optimal path parameterization for critically dynamic motions of humanoid robots, in Proceedings of IEEE-RAS International Conference on Humanoid Robots, 2012, pp. 165–170Google Scholar
  42. 42.
    Y. Zheng, K. Yamane, Adapting human motions to humanoid robots through time warping based on a general motion feasibility index, in IEEE International Conference on Robotics and Automation, 2015, pp. 6281–6288Google Scholar
  43. 43.
    K. Mombaur, A. Truong, J-P. Laumond, From human to humanoid locomotion – an inverse optimal control approach. Auton. Robot. 28(3), 369–383 (2010)CrossRefGoogle Scholar
  44. 44.
    B.D. Argall, S. Chernova, M. Veloso, B. Browning, A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009)CrossRefGoogle Scholar
  45. 45.
    T. Inamura, I. Toshima, H. Tanie, Y. Nakamura, Embodied symbol emergence based on mimesis theory. Int. J. Robot. Res. 23(4), 363–377 (2004)CrossRefGoogle Scholar
  46. 46.
    A.J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, S. Schaal, Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput. 25(2), 328–373 (2013)MathSciNetCrossRefGoogle Scholar
  47. 47.
    S. Calinon, A. Billard, Learning of gestures by imitation in a humanoid robot, in Imitation and Social Learning in Robots, Humans and Animals (Cambridge University Press, Cambridge, 2007), pp. 153–177Google Scholar
  48. 48.
    H. Geyer, H. Herr, A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities. IEEE Trans. Neural Syst. Rehabil. Eng. 18(3), 263–273 (2010)CrossRefGoogle Scholar
  49. 49.
    G. Taga, Y. Yamaguchi, H. Shimizu, Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biol. Cybern. 65, 147–159 (1991)CrossRefGoogle Scholar
  50. 50.
    A.J. Ijspeert, Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 21, 642–653 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Disney ResearchPittsburghUSA
  2. 2.Digital Human Research GroupNational Institute of Advanced Industrial Science and Technology (AIST)TokyoJapan

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