Towards Long-term Autonomy: A Perspective from Robot Learning
Robotics
2023-01-23 v3 Artificial Intelligence
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".
Cite
@article{arxiv.2212.12798,
title = {Towards Long-term Autonomy: A Perspective from Robot Learning},
author = {Zhi Yan and Li Sun and Tomas Krajnik and Tom Duckett and Nicola Bellotto},
journal= {arXiv preprint arXiv:2212.12798},
year = {2023}
}
Comments
Accepted by AAAI-23 Bridge Program on AI & Robotics