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.

翻译：本文提出了一种针对轮式装载机在土堆上执行自动装载动作的世界建模学习方法。通过数据驱动模型，在给定初始料堆形态的高度图及自动铲斗填充控制器的动作参数输入条件下，可预测单次装载循环后的料堆状态、装载质量、作业时间与功耗。该方法将动态变化环境中的序列装载长时程规划转化为重复的模型推理过程。所提出的深度神经网络模型基于超过10,000次随机装载动作的三维多体动力学仿真数据进行训练，这些数据涵盖不同形态的碎石料堆。模型在预测装载性能与料堆状态时，平均准确率分别达到95%（推理时间1.2毫秒）与97%（推理时间4.5毫秒）。实验表明，该方法可实现超过40次连续装载动作的长时程预测。