Soft Robots that Mimic the Neuromusculoskeletal System

Conference paper
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 15)


In motor control studies, the question on which parameters human beings and animals control through their nervous system has been extensively explored and discussed, and several hypotheses proposed. It is widely acknowledged that useful inputs in this problem could be provided by developing artificial replication of the neuromusculoskeletal system, to experiment different motor control hypothesis. In this paper we present such device, which reproduces many of the characteristics of an agonistic-antagonistic muscular pair acting on a joint.


Equilibrium Position Nonlinear Spring External Torque Threshold Length Link Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    A. Albu-Schaffer, O. Eiberger, M. Grebenstein, S. Haddadin, C. Ott, T. Wimbock, S. Wolf, G. Hirzinger, Soft robotics. Robot. Autom. Mag. IEEE 15(3), 20–30 (2008)CrossRefGoogle Scholar
  2. 2.
    M.G. Catalano, G. Grioli, M. Garabini, F. Bonomo, M. Mancinit, N. Tsagarakis, A. Bicchi, Vsa-cubebot: a modular variable stiffness platform for multiple degrees of freedom robots. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pp. 5090–5095. IEEE (2011)Google Scholar
  3. 3.
    A.G. Feldman, Functional tuning of nervous system with control of movement or maintenance of a steady posture. 2. Controllable parameters of muscles. Biophys.-USSR 11(3), 565 (1966)Google Scholar
  4. 4.
    G.A. Feldman, Once more on the equilibrium-point hypothesis (\(\lambda \) model) for motor control. J. Motor Behav. 18(1), 17–54 (1986)CrossRefGoogle Scholar
  5. 5.
    T. Flash, The control of hand equilibrium trajectories in multi-joint arm movements. Biol. Cybern. 57(4–5), 257–274 (1987)CrossRefzbMATHGoogle Scholar
  6. 6.
    L.P. Gribble, D.J. Ostry, V. Sanguineti, R. Laboissière, Are complex control signals required for human arm movement? J. Neurophysiology 79(3), 1409–1424 (1998)Google Scholar
  7. 7.
    G. Grioli, S. Wolf, M. Garabini, M. Catalano, E. Burdet, D. Caldwell, R. Carloni, W. Friedl, M. Grebenstein, M. Laffranchi et al., Variable stiffness actuators: The user?s point of view. Int. J. Robot. Res. 34(6), 727–743 (2015)CrossRefGoogle Scholar
  8. 8.
    J. Morimoto, M. Kawato, Creating the brain and interacting with the brain: an integrated approach to understanding the brain. J. Roy. Soc. Interface 12(104), 20141250 (2015)CrossRefGoogle Scholar
  9. 9.
    P. van der Smagt, Benchmarking cerebellar control. Robot. Auton. Syst. 32(4), 237–251 (2000)CrossRefGoogle Scholar
  10. 10.
    B. Vanderborght, A. Albu-Schäffer, A. Bicchi, E. Burdet, D.G. Caldwell, M. Raffaella Carloni, O.E. Catalano, W. Friedl, G. Ganesh et al., Variable impedance actuators: a review. Robot. Auton. Syst. 61(12), 1601–1614 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.“Enrico Piaggio”, University of PisaPisaItaly
  2. 2.Department of Advanced RoboticsIstituto Italiano di TecnologiaGenovaItaly

Personalised recommendations