  The short-loading cycle is a repetitive task performed in high quantities,
making it a great alternative for automation. In the short-loading cycle, an
expert operator navigates towards a pile, fills the bucket with material,
navigates to a dump truck, and dumps the material into the tipping body. The
operator has to balance the productivity goal while minimising the fuel usage,
to maximise the overall efficiency of the cycle. In addition, difficult
interactions, such as the tyre-to-surface interaction further complicate the
cycle. These types of hard-to-model interactions that can be difficult to
address with rule-based systems, together with the efficiency requirements,
motivate us to examine the potential of data-driven approaches. In this paper,
the possibility of teaching an agent through reinforcement learning to approach
a dump truck's tipping body and get in position to dump material in the tipping
body is examined. The agent is trained in a 3D simulated environment to perform
a simplified navigation task. The trained agent is directly transferred to a
real vehicle, to perform the same task, with no additional training. The
results indicate that the agent can successfully learn to navigate towards the
dump truck with a limited amount of control signals in simulation and when
transferred to a real vehicle, exhibits the correct behaviour.
