The SURE_REACH Model for Motor Learning and Control of a Redundant Arm: From Modeling Human Behavior to Applications in Robotics

  • Oliver Herbort
  • Martin V. Butz
  • Gerulf Pedersen
Part of the Studies in Computational Intelligence book series (SCI, volume 264)


The recently introduced neural network SURE_REACH (sensorimotor unsupervised redundancy resolving control architecture) models motor cortical learning and control of human reaching movements. The model learns redundant, internal body models that are highly suitable to flexibly invoke effective motor commands. The encoded redundancy is used to adapt behavior flexible to situational constraints without the need for further learning. These adaptations to specific tasks or situations are realized by a neurally generated movement plan that adheres to various end-state or trajectory-related constraints. The movement plan can be implemented by proprioceptive or visual closed-loop control. This chapter briefly reviews the literature on computational models of motor learning and control and gives a description of SURE_REACH and its neural network implementation. Furthermore, we relate the model to human motor learning and performance and discuss its neural foundations. Finally, we apply the model to the control of a dynamic robot platform. In sum, SURE_REACH grounds highly flexible task-dependent behavior on a neural network framework for unsupervised learning. It accounts for the neural processes that underlie fundamental aspects of human behavior and is well applicable to the control of robots.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Oliver Herbort
    • 1
  • Martin V. Butz
    • 1
  • Gerulf Pedersen
    • 1
  1. 1.Department of PsychologyUniversität WürzburgWürzburgGermany

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