论文标题
在现实世界中达到自主分级
Towards Autonomous Grading In The Real World
论文作者
论文摘要
在这项工作中，我们旨在解决自动分级问题，在这种情况下，必须将推土机弄平不平衡的区域。此外，我们探索了弥合模拟环境和实际场景之间差距的方法。我们设计了一个逼真的物理模拟，也是模仿真实推土机动力学和感官信息的缩放的真实原型环境。我们建立了启发式方法和学习策略，以解决问题。通过广泛的实验，我们表明，尽管启发式方法能够在清洁且无噪音的模拟环境中解决该问题，但在面对现实世界情景时，它们会灾难性地失败。由于启发式方法能够在模拟环境中成功解决任务，因此我们表明它们可以被利用来指导学习代理，该学习代理可以在模拟和缩放原型环境中概括和解决任务。
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.
