Lifelong Navigation
By Bo Liu, Xuesu Xiao, Peter Stone. 2020
10 citations recorded by the source corpus.
This paper presents a continually 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 a lifelong learning framework for mobile robot navigation which (1) improves from its own experience without expert demonstrations, and (2) retains capability to navigate in previous environments after learning in new ones. This framework is implemented and tested entirely onboard a physical robot with a limited memory and computation budget.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
In Computer Vision and Pattern Recognition
- Yen-Chang Hsu
- Zsolt Kira
- Hongxia Jin
- Yilin Shen
- James Smith
- Jonathan C. Balloch
- Yixuan Li
- Andrew Zisserman
Citations in the corpus
- Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning (2021, background): “This is concerning for many computer vision applications because 1) Many computer vision applications are on-device and therefore memory constrained [17, 37, 54], and 2) Many computer vision applications learn from data which cannot be legally stored [6, 12, 64].”