Towards Long-term Autonomy: A Perspective from Robot Learning

TL;DR

This paper discusses the importance of online robot learning for achieving long-term autonomy in service robots operating in dynamic environments, emphasizing the need for adaptive, on-site learning capabilities.

Contribution

It provides a perspective on long-term robot autonomy focusing on online learning methods and their role in enabling adaptive, sustained operation in changing environments.

Findings

Highlights the importance of on-site, online learning for long-term autonomy.

Discusses challenges and considerations in deploying adaptive learning in robots.

Emphasizes the interplay between data collection and deployment in autonomous systems.

Abstract

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".