  Programming a robot to deal with open-ended tasks remains a challenge, in
particular if the robot has to manipulate objects. Launching, grasping, pushing
or any other object interaction can be simulated but the corresponding models
are not reversible and the robot behavior thus cannot be directly deduced.
These behaviors are hard to learn without a demonstration as the search space
is large and the reward sparse. We propose a method to autonomously generate a
diverse repertoire of simple object interaction behaviors in simulation. Our
goal is to bootstrap a robot learning and development process with limited
information about what the robot has to achieve and how. This repertoire can be
exploited to solve different tasks in reality thanks to a proposed adaptation
method or could be used as a training set for data-hungry algorithms.
  The proposed approach relies on the definition of a goal space and generates
a repertoire of trajectories to reach attainable goals, thus allowing the robot
to control this goal space. The repertoire is built with an off-the-shelf
simulation thanks to a quality diversity algorithm. The result is a set of
solutions tested in simulation only. It may result in two different problems:
(1) as the repertoire is discrete and finite, it may not contain the trajectory
to deal with a given situation or (2) some trajectories may lead to a behavior
in reality that differs from simulation because of a reality gap. We propose an
approach to deal with both issues by using a local linearization of the mapping
between the motion parameters and the observed effects. Furthermore, we present
an approach to update the existing solutions repertoire with the tests done on
the real robot. The approach has been validated on two different experiments on
the Baxter robot: a ball launching and a joystick manipulation tasks.
