Tacit Learning for Emergence of Task-Related Behaviour through Signal Accumulation

  • Vincent Berenz
  • Fady Alnajjar
  • Mitsuhiro Hayashibe
  • Shingo Shimoda
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 316)


Control of robotic joints movements requires the generation of appropriate torque and force patterns, coordinating the kinematically and dynamically complex multijoints systems. Control theory coupled with inverse and forward internal models are commonly used to map a desired endpoint trajectory into suitable force patterns. In this paper, we propose the use of tacit learning to successfully achieve similar tasks without using any kinematic model of the robotic system to be controlled. Our objective is to design a new control strategy that can achieve levels of adaptability similar to those observed in living organisms and be plausible from a neural control viewpoint. If the neural mechanisms used for mapping goals expressed in the task-space into control-space related command without using internal models remain largely unknown, many neural systems rely on data accumulation. The presented controller does not use any internal model and incorporates knowledge expressed in the task space using only the accumulation of data. Tested on a simulated two-link robot system, the controller showed flexibility by developing and updating its parameters through learning. This controller reduces the gap between reflexive motion based on simple accumulation of data and execution of voluntarily planned actions in a simple manner that does not require complex analysis of the dynamics of the system.


Robotic System Internal Model Mapping Goal Task Space Stability Line 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Vincent Berenz
    • 1
  • Fady Alnajjar
    • 1
  • Mitsuhiro Hayashibe
    • 2
  • Shingo Shimoda
    • 1
  1. 1.BSI-Toyota Collaboration Center, RIKENNagoyaJapan
  2. 2.INRIA DEMAR Project and LIRMM, UMR5506CNRS University of MontpellierMontpellierFrance

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