A Lifelong Learning Approach o Mobile Robot Navigation
A Lifelong Learning Approach o Mobile Robot Navigation
复制标题
移动机器人导航的终身学习方法
DOI：
10.1109/lra.2021.3056373
复制
发表时间：
2021-04-01
影响因子：
5.2
通讯作者：
Stone, Peter
中科院分区：
文献类型：
--
作者：
Liu, Bo;Xiao, Xuesu;Stone, Peter
这封信提出了一个自我改进的终身学习框架，用于移动机器人在不同环境中导航。经典的静态导航方法需要环境特定的原位系统调整，例如，来自人类专家，或者可能重复他们的错误，无论他们在相同的环境中导航了多少次。基于学习的导航具有随着经验而改进的潜力，高度依赖于获得训练资源，例如足够的内存和快速的计算，并且容易忘记先前学习的能力，特别是在面对不同的环境时。在这项工作中，我们提出了终身学习导航（LLfN），它(1)纯粹基于自己的经验来提高移动机器人的导航行为，(2)在新环境中学习后保留机器人在以前环境中的导航能力。LLfN完全是在一个内存和计算预算有限的物理机器人上实现和测试的。
This letter 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.