论文标题
QUAD2Plane：通过退缩地平线控制在线机器人技术中在线勘探的中级培训程序
Quad2Plane: An Intermediate Training Procedure for Online Exploration in Aerial Robotics via Receding Horizon Control
论文作者
论文摘要
数据驱动的机器人技术依赖于准确的现实代表来学习有用的策略。尽管我们最好的努力，但零射击的SIM到运行转移仍然是一个未解决的问题，我们通常需要允许我们的代理商在线探索以学习给定任务的有用策略。对于许多现场机器人技术的应用，在线探索的昂贵且危险性高昂，尤其是在固定翼空中机器人技术中确实如此。为了应对这些挑战，我们为实地机器人技术提供了一种中介解决方案。我们调查使用不同平台工具来学习的使用，并提供了一个程序，以模仿另一种车辆的行为。我们专门考虑使用多转机主机平台训练固定翼飞机（一种昂贵且危险的车辆类型）的问题。使用模型预测控制方法，我们设计了一个能够模仿模拟和现实世界中其他车辆行为的控制器。
Data driven robotics relies upon accurate real-world representations to learn useful policies. Despite our best-efforts, zero-shot sim-to-real transfer is still an unsolved problem, and we often need to allow our agents to explore online to learn useful policies for a given task. For many applications of field robotics online exploration is prohibitively expensive and dangerous, this is especially true in fixed-wing aerial robotics. To address these challenges we offer an intermediary solution for learning in field robotics. We investigate the use of dissimilar platform vehicle for learning and offer a procedure to mimic the behavior of one vehicle with another. We specifically consider the problem of training fixed-wing aircraft, an expensive and dangerous vehicle type, using a multi-rotor host platform. Using a Model Predictive Control approach, we design a controller capable of mimicking another vehicles behavior in both simulation and the real-world.
