In this work, we aim to tackle the problem of autonomous grading, where a dozer is required to flatten an uneven area. In addition, we explore methods for bridging the gap between a simulated environment and real scenarios. We design both a realistic physical simulation and a scaled real prototype environment mimicking the real dozer dynamics and sensory information. We establish heuristics and learning strategies in order to solve the problem. Through extensive experimentation, we show that although heuristics are capable of tackling the problem in a clean and noise-free simulated environment, they fail catastrophically when facing real world scenarios. As the heuristics are capable of successfully solving the task in the simulated environment, we show they can be leveraged to guide a learning agent which can generalize and solve the task both in simulation and in a scaled prototype environment.

翻译：在这项工作中,我们的目标是解决自动定级问题,在这个问题上,需要用 dozer 来平平一个不平衡的区域。此外,我们探索弥合模拟环境和真实情景之间差距的方法。我们设计现实的物理模拟和一个规模化的实际原型环境,模仿真正的 dozer 动态和感官信息。我们建立休眠和学习战略来解决这个问题。通过广泛的实验,我们证明尽管超自然学能够在一个清洁和无噪音的模拟环境中解决问题,但在面对现实世界情景时却无法成功解决问题。由于超自然学能够成功地解决模拟环境中的任务,我们证明它们能够被利用来引导一种学习的媒介,既可以在模拟中,也可以在一个规模化的原型环境中推广和解决这项任务。