Towards Long-term Autonomy: A Perspective from Robot Learning

Yan, Zhi, Sun, Li, Krajnik, Tomas , Duckett, Tom and Bellotto, Nicola (2023) Towards Long-term Autonomy: A Perspective from Robot Learning. In: AAAI Bridge Program “AI and Robotics”, 7 February 2023, Washington, DC, USA.

Documents
Towards Long-term Autonomy: A Perspective from Robot Learning
[img] PDF
2212.12798.pdf - Whole Document
Restricted to Repository staff only

409kB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

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".

Keywords:Robotics, Machine Learning
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:53115
Deposited On:20 Feb 2023 12:26

Repository Staff Only: item control page