Autonomous Color Learning on a Mobile Robot
Abstract
Color segmentation is a challenging subtask in computer vi-sion. Most popular approaches are computationally expensive, involve an extensive off-line training phase and/or rely on a sta-tionary camera. This paper presents an approach for color learn-ing on-board a legged robot with limited computational and memory resources. A key defining feature of the approach is that it works without any labeled training data. Rather, it trains autonomously from a color-coded model of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy.
Cite
Text
Sridharan and Stone. "Autonomous Color Learning on a Mobile Robot." AAAI Conference on Artificial Intelligence, 2005.Markdown
[Sridharan and Stone. "Autonomous Color Learning on a Mobile Robot." AAAI Conference on Artificial Intelligence, 2005.](https://mlanthology.org/aaai/2005/sridharan2005aaai-autonomous/)BibTeX
@inproceedings{sridharan2005aaai-autonomous,
title = {{Autonomous Color Learning on a Mobile Robot}},
author = {Sridharan, Mohan and Stone, Peter},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2005},
pages = {1318-1323},
url = {https://mlanthology.org/aaai/2005/sridharan2005aaai-autonomous/}
}