  We have recently proposed two pile loading controllers that learn from human
demonstrations: a neural network (NNet) [1] and a random forest (RF) controller
[2]. In the field experiments the RF controller obtained clearly better success
rates. In this work, the previous findings are drastically revised by
experimenting summer time trained controllers in winter conditions. The winter
experiments revealed a need for additional sensors, more training data, and a
controller that can take advantage of these. Therefore, we propose a revised
neural controller (NNetV2) which has a more expressive structure and uses a
neural attention mechanism to focus on important parts of the sensor and
control signals. Using the same data and sensors to train and test the three
controllers, NNetV2 achieves better robustness against drastically changing
conditions and superior success rate. To the best of our knowledge, this is the
first work testing a learning-based controller for a heavy-duty machine in
drastically varying outdoor conditions and delivering high success rate in
winter, being trained in summer.
