In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emerging with the development of robotics, both hardware and software. Today we believe that an important underpinning of long-term robot autonomy is the ability of robots to learn on site and on-the-fly, especially when they are deployed in changing environments or need to traverse different environments. In this paper, we examine the problem of long-term autonomy from the perspective of robot learning, especially in an online way, and discuss in tandem its premise "data" and the subsequent "deployment".

翻译：未来,服务机器人可以在没有人类干预的情况下长期自主运行。随着硬件和软件等机器人的开发,许多为实现这一目标而努力的工作已经出现。今天,我们认为,长期机器人自主的一个重要基础是机器人在现场和现场学习的能力,特别是当机器人被部署在不断变化的环境中或需要穿越不同的环境时。本文从机器人学习的角度,特别是在线学习的角度,审视长期自主的问题,并结合其“数据”和随后的“部署”的前提,讨论其“数据”和“部署”问题。