Title: A Non-linear Approach to Space Dimension Perception by a Naive Agent,
Abstract: Developmental Robotics offers a new approach to numerous AI features that are
often taken as granted. Traditionally, perception is supposed to be an inherent
capacity of the agent. Moreover, it largely relies on models built by the
system's designer. A new approach is to consider perception as an
experimentally acquired ability that is learned exclusively through the
analysis of the agent's sensorimotor flow. Previous works, based on
H.Poincaré's intuitions and the sensorimotor contingencies theory, allow a
simulated agent to extract the dimension of geometrical space in which it is
immersed without any a priori knowledge. Those results are limited to
infinitesimal movement's amplitude of the system. In this paper, a non-linear
dimension estimation method is proposed to push back this limitation.