  In recent years Sim2Real approaches have brought great results to robotics.
Techniques such as model-based learning or domain randomization can help
overcome the gap between simulation and reality, but in some situations
simulation accuracy is still needed. An example is agricultural robotics, which
needs detailed simulations, both in terms of dynamics and visuals. However,
simulation software is still not capable of such quality and accuracy. Current
Sim2Real techniques are helpful in mitigating the problem, but for these
specific tasks they are not enough.
