  This paper presents a method for learning world models for wheel loaders
performing automatic loading actions on a pile of soil. Data-driven models were
learned to output the resulting pile state, loaded mass, time, and work for a
single loading cycle given inputs that include a heightmap of the initial pile
shape and action parameters for an automatic bucket-filling controller.
Long-horizon planning of sequential loading in a dynamically changing
environment is thus enabled as repeated model inference. The models, consisting
of deep neural networks, were trained on data from 3D multibody dynamics
simulation of over 10,000 random loading actions in gravel piles of different
shapes. The accuracy and inference time for predicting the loading performance
and the resulting pile state were, on average, 95% in 1.2 ms and 97% in 4.5 ms,
respectively. Long-horizon predictions were found feasible over 40 sequential
loading actions.
