Autonomous wheel loading involves selecting actions that maximize the total
performance over many repetitions. The actions should be well adapted to the
current state of the pile and its future states. Selecting the best actions is
difficult since the pile states are consequences of previous actions and thus
are highly unknown. To aid the selection of actions, this paper investigates
data-driven models to predict the loaded mass, time, work, and resulting pile
state of a loading action given the initial pile state. Deep neural networks
were trained on data using over 10,000 simulations to an accuracy of 91-97,%
with the pile state represented either by a heightmap or by its slope and
curvature. The net outcome of sequential loading actions is predicted by
repeating the model inference at five milliseconds per loading. As errors
accumulate during the inferences, long-horizon predictions need to be combined
with a physics-based model.Comment: 22 pages, 19 figure