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Abstract
  • While internal models are a prerequisite for
    higher-level function, they have to be grounded in lowerlevel
    function serving sensorimotor control. In this paper we
    introduce an internal body model for the control of a hexapod
    walker. The internal model deals with a highly complex robotic
    structure of 22 degrees of freedom and coordinates the single
    joint movements to achieve an overall stable and adaptive
    walking behavior. It is implemented as a hierarchical recurrent
    neural network consisting of different levels of abstraction
    which are tightly intertwined. We demonstrate the feasibility
    of the concept by applying the model to a simulated robot
    and show how the different levels of the body model interact
    and how this allows to scale the model even further. While
    the internal model is used in this context explicitly for motor
    control, it is also a predictive model and can be applied for
    sensor fusion. We discuss how in this way such an internal
    model offers the flexibility to be utilized in motor control and
    to be used for planning ahead by a cognitive expansion of the
    movement controller.
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