Title: Learning an internal representation of the end-effector configuration space,
Abstract: Current machine learning techniques proposed to automatically discover a
robot kinematics usually rely on a priori information about the robot's
structure, sensors properties or end-effector position. This paper proposes a
method to estimate a certain aspect of the forward kinematics model with no
such information. An internal representation of the end-effector configuration
is generated from unstructured proprioceptive and exteroceptive data flow under
very limited assumptions. A mapping from the proprioceptive space to this
representational space can then be used to control the robot.