Learning off-road driving from simulation

在加拿大的地形上驾驶越野车辆是我们许多林业、建筑和采矿工人的一项技能，但由于打滑、打滑和翻车，这也对人和设备的安全构成了巨大风险。CM Labs生产世界领先的培训和模拟产品，允许重型设备操作员在类似游戏的软件体验中安全地面对此类情况，而不会带来人身风险，然而，许多最具挑战性的环境和快速运动仍无法通过我们最先进的模型来捕捉。该项目与麦吉尔移动机器人实验室合作，将利用强化学习的最新进展来制定新的越野驾驶策略，并进一步改进CM实验室的轮胎-地形动力学模型。这项工作的一个关键方面是智能软件仅在模拟中探索驾驶可能性的能力，为此我们将依赖CM Lab的Vortex Studio与人工智能最新进展的集成。然后，这一近似领域的研究结果将被转移到McGill最近制造的小型灵活户外车辆上，以验证模型并找出我们的模拟器与现实之间的差异。结果将提高加拿大空间的户外自动化，以及CM实验室培训我们的员工即使在加拿大最具挑战性的环境中安全操作的能力。

Driving vehicles off-road in Canadian terrain is a skill of many of our forestry, construction and mining workforce, but it also poses great risk to human and equipment safety due to sliding, skidding and roll-over. CM Labs produces world-leading training and simulation products that allow operators of heavy equipment to face such situations in the safety of a game-like software experience, without personal risk, however, many of the most challenging environments and rapid motions cannot yet be captured by our state-of-the-art models. In collaboration with McGill's Mobile Robotics Lab, this project will leverage recent advances in Reinforcement Learning to produce new off-road driving policies and further improve CM Labs' tire-terrain dynamics models. A key aspect of this work is the ability of a intelligent software to explore driving possibilities only in simulation, for which we will rely on integration of CM Lab's Vortex Studio with the most recent advances in AI. Findings in this approximate domain will then be transferred to small and agile outdoor vehicles built recently at McGill, to validate the models and find differences between our simulators and reality. The results will improve outdoor automation in Canadian spaces, and the ability of CM Labs to train our workforce to safely conduct operations even in Canada's most challenging environments.