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Proceedings Paper

An analysis on data curation using mobile robots for learning tasks in complex environments
Author(s): Julia Donlon; Matthew Young; Maggie Wigness; Cory Hayes
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Paper Abstract

Commercial Artificial Intelligence (AI), e.g., the self driving car industry, is often used in predictable settings, with structured surroundings. Significant AI and Machine Learning (ML) progress, particularly in visual perception, has been made in these settings with the use of large publicly available datasets. However, there still exists a prevalent domain mismatch between this data and military relevant environments. In this work we begin to analyze the importance of mobile robot platform design and heterogeneity to effectively collect data more representative of the military domain. The framework of our research is rooted in the importance of expressing constantly changing, yet repeated conditions, with disadvantageous lighting and perspectives in highly unstructured environments.

Paper Details

Date Published: 10 May 2019
PDF: 9 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060H (10 May 2019); doi: 10.1117/12.2518546
Show Author Affiliations
Julia Donlon, U.S. Army Research Lab. (United States)
Matthew Young, U.S. Army Research Lab. (United States)
Maggie Wigness, U.S. Army Research Lab. (United States)
Cory Hayes, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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