Neural Network Controller for Autonomous Pile Loading Revised
This work addresses the challenge of deploying learning-based controllers for heavy-duty machines in real-world, changing environments, though it is incremental as it builds on prior controllers.
The paper tackles the problem of autonomous pile loading in drastically varying outdoor conditions by revising a neural network controller to improve robustness and success rate, achieving superior performance in winter conditions despite being trained in summer.
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.