  In this work, we aim to tackle the problem of autonomous grading, where a
dozer is required to flatten an uneven area. In addition, we explore methods
for bridging the gap between a simulated environment and real scenarios. We
design both a realistic physical simulation and a scaled real prototype
environment mimicking the real dozer dynamics and sensory information. We
establish heuristics and learning strategies in order to solve the problem.
Through extensive experimentation, we show that although heuristics are capable
of tackling the problem in a clean and noise-free simulated environment, they
fail catastrophically when facing real world scenarios. As the heuristics are
capable of successfully solving the task in the simulated environment, we show
they can be leveraged to guide a learning agent which can generalize and solve
the task both in simulation and in a scaled prototype environment.
