Deep Learning For Robotics

Other Titles
Abstract

Robotics faces many unique challenges as robotic platforms move out of the lab and into the real world. In particular, the huge amount of variety encountered in real-world environments is extremely challenging for existing robotic control algorithms to handle. This necessistates the use of machine learning algorithms, which are able to learn controls given data. However, most conventional learning algorithms require hand-designed parameterized models and features, which are infeasible to design for many robotic tasks. Deep learning algorithms are general non-linear models which are able to learn features directly from data, making them an excellent choice for such robotics applications. However, care must be taken to design deep learning algorithms and supporting systems appropriate for the task at hand. In this work, I describe two applications of deep learning algorithms and one application of hardware neural networks to difficult robotics problems. The problems addressed are robotic grasping, food cutting, and aerial robot obstacle avoidance, but the algorithms presented are designed to be generalizable to related tasks.

Journal / Series
Volume & Issue
Description
Sponsorship
Date Issued
2016-02-01
Publisher
Keywords
Robotics; Machine learning; Deep learning
Location
Effective Date
Expiration Date
Sector
Employer
Union
Union Local
NAICS
Number of Workers
Committee Chair
Saxena,Ashutosh
Committee Co-Chair
Committee Member
Snavely,Keith Noah
Manohar,Rajit
Knepper,Ross A
Degree Discipline
Computer Science
Degree Name
Ph. D., Computer Science
Degree Level
Doctor of Philosophy
Related Version
Related DOI
Related To
Related Part
Based on Related Item
Has Other Format(s)
Part of Related Item
Related To
Related Publication(s)
Link(s) to Related Publication(s)
References
Link(s) to Reference(s)
Previously Published As
Government Document
ISBN
ISMN
ISSN
Other Identifiers
Rights
Rights URI
Types
dissertation or thesis
Accessibility Feature
Accessibility Hazard
Accessibility Summary
Link(s) to Catalog Record