论文标题
移动机器人导航的终身学习方法
A Lifelong Learning Approach to Mobile Robot Navigation
论文作者
论文摘要
本文为在不同环境中导航的移动机器人提供了一个自我改善的终身学习框架。经典的静态导航方法需要特定环境的原位系统调整，例如来自人类专家，或者可能会重复自己的错误，而不管他们在同一环境中导航了多少次。具有改善经验的潜力，基于学习的导航在很大程度上取决于获得培训资源的访问，例如足够的内存和快速计算，很容易忘记以前学习的能力，尤其是在面对不同环境时。在这项工作中，我们提出了终生的导航学习（LLFN），（1）纯粹基于其自身的经验来改善移动机器人的导航行为，并且（2）保留机器人在学习后在以前的环境中在以前的环境中导航的能力。 LLFN的实施和测试是在机器人机器人上的内存和计算预算有限的物理机器人上进行的。
This paper presents a self-improving lifelong learning framework for a mobile robot navigating in different environments. Classical static navigation methods require environment-specific in-situ system adjustment, e.g. from human experts, or may repeat their mistakes regardless of how many times they have navigated in the same environment. Having the potential to improve with experience, learning-based navigation is highly dependent on access to training resources, e.g. sufficient memory and fast computation, and is prone to forgetting previously learned capability, especially when facing different environments. In this work, we propose Lifelong Learning for Navigation (LLfN) which (1) improves a mobile robot's navigation behavior purely based on its own experience, and (2) retains the robot's capability to navigate in previous environments after learning in new ones. LLfN is implemented and tested entirely onboard a physical robot with a limited memory and computation budget.
