Flexible internal body models for motor control: On the convergence of constrained dual quaternion mean of multiple computation networks

While internal models are recruited in many tasks and can subserve in this way perception and cognition, it is important that they are grounded and embodied in sensorimotor representation. In this paper we analyze an internal model of the body and show how it can be used for motor control. We extend the Mean of Multiple Computation principle to a dual quaternion representation of transformation and show how this can be directly applied to the control of a simulated robot leg. The model is encoded as a recurrent neural network acting as an autoassociator that is able to solve any kinematic problem in an iterative fashion. We will analyze the convergence properties, especially when additional constraints (acting on the joint level) are introduced that restrict the attractor space.