Robotic systems are ever more capable of automation
and fulfilment of complex tasks, particularly with
reliance on recent advances in intelligent systems, deep learning
and artificial intelligence in general. However, as robots and
humans come closer together in their interactions, the matter
of interpretability, or explainability of robot decision-making
processes for the human grows in importance. A successful
interaction and collaboration would only be possible through
mutual understanding of underlying representations of the
environment and the task at hand. This is currently a challenge
in deep learning systems. We present a hierarchical deep
reinforcement learning system, consisting of a low-level agent
handling the large actions/states space of a robotic system
efficiently, by following the directives of a high-level agent which
is learning the high-level dynamics of the environment and task.
This high-level agent forms a representation of the world and
task at hand that is interpretable for a human operator. The
method, which we call Dot-to-Dot, is tested on a MuJoCo-based
model of the Fetch Robotics Manipulator, as well as a Shadow
Hand, to test its performance. Results show efficient learning
of complex actions/states spaces by the low-level agent, and an
interpretable representation of the task and decision-making
process learned by the high-level agent.