论文标题
appld：自适应计划者参数从演示中学习
APPLD: Adaptive Planner Parameter Learning from Demonstration
论文作者
论文摘要
现有的自主机器人导航系统允许机器人以无冲突的方式从一个点移到另一点。但是，在面对新环境时，这些系统通常需要由专家机器人的专家重新调查，并可以很好地了解导航系统的内部工作。相比之下，即使是在机器人导航算法详细信息中不关心的用户也可以通过远程处理在新环境中产生理想的导航行为。在本文中，我们从演示中介绍了AppLD，自适应计划者参数学习，该参数允许现有的导航系统成功应用于新的复杂环境中，仅鉴于人类的详细示范是理想的导航。在两个在不同环境中运行不同导航系统的机器人上验证了APPLD。实验结果表明，APPLD可以超越默认和专家调整的参数，甚至是人类演示者本身的导航系统。
Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner. However, when facing new environments, these systems generally require re-tuning by expert roboticists with a good understanding of the inner workings of the navigation system. In contrast, even users who are unversed in the details of robot navigation algorithms can generate desirable navigation behavior in new environments via teleoperation. In this paper, we introduce APPLD, Adaptive Planner Parameter Learning from Demonstration, that allows existing navigation systems to be successfully applied to new complex environments, given only a human teleoperated demonstration of desirable navigation. APPLD is verified on two robots running different navigation systems in different environments. Experimental results show that APPLD can outperform navigation systems with the default and expert-tuned parameters, and even the human demonstrator themselves.
