Manifold Learning in Robotics: A Tutorial and Survey

Author

• marcus hawkins, University of Texas at ArlingtonFollow

Graduation Semester and Year

Spring 2024

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

William J. Beksi

Second Advisor

Keaton Hamm

Abstract

In this article, we hope to represent the current state of the art of manifold learning in an understandable and approachable way. The authors will present a general overview core algorithms associated with linear and nonlinear dimensionality reduction techniques, give rudimentary definitions from differential geometry, and tenets of robotic perception, manipulation and path planning. Some of the historical applications of these algorithms will be presented, as well as conjectures about future uses, through examples from peer-reviewed journals.

Keywords

Manifold, NLDR, Manifold learning

Disciplines

Data Science | Geometry and Topology | Other Mathematics | Robotics

License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Recommended Citation

hawkins, marcus, "Manifold Learning in Robotics: A Tutorial and Survey" (2024). Computer Science and Engineering Theses - Archive. 3.
https://mavmatrix.uta.edu/cse_theses/3