We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain. The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites. The controller displays the ability to handle obstructing obstacles, slopes up to 27$^\circ$, and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions. The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.
翻译:在人类操作者和传统控制方法不足的情况下,我们探讨利用深度增援来控制地形车辆的可能性。本信展示了一名控制器,它能感知、规划和成功地控制16吨林车,配有两条架形接头、6个轮轮和它们积极阐述的悬浮,以绕过粗地。精心塑造的奖励信号能促进安全、环境和高效驾驶,从而导致前所未有的驾驶技能的出现。我们在虚拟环境中测试学习的技能,包括从高密度激光扫描森林场地而重建的地形。控制器显示有能力处理阻塞障碍、高达27 ⁇ circ$的斜坡以及各种自然地形,所有这些地形都有有限的轮滑、光滑和直径,并明智地使用主动悬浮。结果证实深重力学习有可能加强与人类操作者或传统控制方法相比具有复杂动态的车辆和高维观测数据的控制,特别是在粗地。