Hierarchical Affordance Discovery using Intrinsic Motivation
  To be capable of lifelong learning in a real-life environment, robots have to
tackle multiple challenges. Being able to relate physical properties they may
observe in their environment to possible interactions they may have is one of
them. This skill, named affordance learning, is strongly related to embodiment
and is mastered through each person's development: each individual learns
affordances differently through their own interactions with their surroundings.
Current methods for affordance learning usually use either fixed actions to
learn these affordances or focus on static setups involving a robotic arm to be
operated. In this article, we propose an algorithm using intrinsic motivation
to guide the learning of affordances for a mobile robot. This algorithm is
capable to autonomously discover, learn and adapt interrelated affordances
without pre-programmed actions. Once learned, these affordances may be used by
the algorithm to plan sequences of actions in order to perform tasks of various
difficulties. We then present one experiment and analyse our system before
comparing it with other approaches from reinforcement learning and affordance
learning.
