Interaction Force, Impedance and Trajectory Adaptation: By Humans, for Robots

  • Etienne Burdet
  • Gowrishankar Ganesh
  • Chenguang Yang
  • Alin Albu-Schäffer
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)

Abstract

This paper develops and analyses a biomimetic learning controller for robots. This controller can simultaneously adapt reference trajectory, impedance and feedforward force to maintain stability and minimize the weighted summation of interaction force and performance errors. This controller was inspired from our studies of human motor behavior, especially the human motor control approach dealing with unstable situations typical of tool use. Simulations show that the developed controller is a good model of human motor adaptation. Implementations demonstrate that it can also utilise the capabilities of joint torque controlled robots and variable impedance actuators to optimally adapt interaction with dynamic environments and humans.

Keywords

Reference Trajectory Human Motor Functional Electrical Stimulation Haptic Device Experimental Brain Research 
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.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Etienne Burdet
    • 1
  • Gowrishankar Ganesh
    • 2
  • Chenguang Yang
    • 1
    • 3
  • Alin Albu-Schäffer
    • 4
  1. 1.Imperial College LondonLondonUK
  2. 2.Biological ICT groupNICT, and CNS-ATR JapanKyotoJapan
  3. 3.University of PlymouthPlymouthUK
  4. 4.DLRWesslingGermany

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