  Robotics has been a popular field of research in the past few decades, with
much success in industrial applications such as manufacturing and logistics.
This success is led by clearly defined use cases and controlled operating
environments. However, robotics has yet to make a large impact in domestic
settings. This is due in part to the difficulty and complexity of designing
mass-manufactured robots that can succeed in the variety of homes and
environments that humans live in and that can operate safely in close proximity
to humans. This paper explores the use of contextual affordances to enable safe
exploration and learning in robotic scenarios targeted in the home. In
particular, we propose a simple state representation that allows us to extend
contextual affordances to larger state spaces and showcase how affordances can
improve the success and convergence rate of a reinforcement learning algorithm
in simulation. Our results suggest that after further iterations, it is
possible to consider the implementation of this approach in a real robot
manipulator. Furthermore, in the long term, this work could be the foundation
for future explorations of human-robot interactions in complex domestic
environments. This could be possible once state-of-the-art robot manipulators
achieve the required level of dexterity for the described affordances in this
paper.
