Autonomous Robots

, Volume 29, Issue 3–4, pp 357–366 | Cite as

Using efference copy and a forward internal model for adaptive biped walking

  • Johannes Schröder-Schetelig
  • Poramate Manoonpong
  • Florentin WörgötterEmail author
Open Access


To behave properly in an unknown environment, animals or robots must distinguish external from self-generated stimuli on their sensors. The biologically inspired concepts of efference copy and internal model have been successfully applied to a number of robot control problems. Here we present an application of this for our dynamic walking robot RunBot. We use efference copies of the motor commands with a simple forward internal model to predict the expected self-generated acceleration during walking. The difference to the actually measured acceleration is then used to stabilize the walking on terrains with changing slopes through its upper body component controller. As a consequence, the controller drives the upper body component (UBC) to lean forwards/backwards as soon as an error occurs resulting in dynamical stable walking. We have evaluated the performance of the system on four different track configurations. Furthermore we believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots.


Efference copy Forward internal model Neural network Biped robot Dynamic walking Walking machine 

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© The Author(s) 2010

Open AccessThis is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Johannes Schröder-Schetelig
    • 1
  • Poramate Manoonpong
    • 2
  • Florentin Wörgötter
    • 2
    Email author
  1. 1.Max Planck Institute for Dynamics and Self-OrganizationGöttingenGermany
  2. 2.Bernstein Center for Computational Neuroscience (BCCN)University of GöttingenGöttingenGermany

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