论文标题
从经验中学习机器人导航：原理，方法和最新结果
Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results
论文作者
论文摘要
导航是机器人技术中研究最广泛的问题之一，通常被视为几何映射和计划问题。但是，现实世界导航提出了一系列复杂的物理挑战，这些挑战违反了简单的几何抽象。机器学习提供了一种有希望的方法，可以超越几何形状和传统计划，从而为基于实际先前经验做出决定的导航系统。这样的系统可以以超越几何形状的方式来理解遍历性，这是其行动的物理结果以及在现实环境中利用模式的物理结果。随着收集更多的数据，它们也可以改进，并可能提供强大的网络效应。在本文中，我们提出了一个通用工具包，用于对机器人导航技能的体验学习，该工具包统一了最近的几种方法，描述了潜在的设计原理，总结了我们最近的几篇论文的实验结果，并讨论了未来工作的开放问题和方向。
Navigation is one of the most heavily studied problems in robotics, and is conventionally approached as a geometric mapping and planning problem. However, real-world navigation presents a complex set of physical challenges that defies simple geometric abstractions. Machine learning offers a promising way to go beyond geometry and conventional planning, allowing for navigational systems that make decisions based on actual prior experience. Such systems can reason about traversability in ways that go beyond geometry, accounting for the physical outcomes of their actions and exploiting patterns in real-world environments. They can also improve as more data is collected, potentially providing a powerful network effect. In this article, we present a general toolkit for experiential learning of robotic navigation skills that unifies several recent approaches, describe the underlying design principles, summarize experimental results from several of our recent papers, and discuss open problems and directions for future work.
