Towards Autonomous Grading in the Real World

Yakov Miron, Yuval Goldfracht, Dotan Di Castro

Abstract: Surface grading is an integral part of the construction pipeline. Here, a dozer, which is a key machinery tool in any construction site, is required to level an uneven area containing pre-dumped sand piles. In this work, we aim to tackle the problem of autonomous surface grading on real-world scenarios. We design both a realistic physical simulation and a scaled real-world 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. However, we show that the simulation can be leveraged to guide a learning agent, which can generalize and solve the task both in simulation and in a scaled prototype environment.

External IDs:dblp:conf/eccv/MironGC22