Ph.D. Dissertation Defense - Rowland O'Flaherty

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Event Details
  • Date/Time:
    • Monday December 7, 2015 - Tuesday December 8, 2015
      7:00 pm - 6:59 pm
  • Location: Room 530, TSRB
  • Phone:
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  • Fee(s):
    N/A
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Summaries

Summary Sentence: ROBOTICS PhD Dissertation Defense

Full Summary: No summary paragraph submitted.

Title: A Control Theoretic Perspective on Learning in Robotics

Committee:Dr. Magnus Egerstedt (Advisor), School of Electrical and Computer Engineering, Georgia Tech
Dr. Ayanna Howard, School of Electrical and Computer Engineering, Georgia Tech
Dr. Patricio Vela, School of Electrical and Computer Engineering, Georgia Tech
Dr. Jonathan Rogers, School of Mechanical Engineering, Georgia Tech
Dr. Charles Isbell, School of Interactive Computing, Georgia Tech
Abstract:
For robotic systems to continue to move towards ubiquity, robots need to be more autonomous. More autonomy dictates that robots need to be able to make better decisions. Control theory and machine learning are fields of robotics that focus on the decision making process. However, each of these fields implements decision making at different levels of abstraction and at different time scales. Control theory defines low-level decisions at high rates, while machine learning defines high-level decision at low rates. The objective of this research is to integrate tools from both machine leaning and control theory to solve higher dimensional, complex problems, and to optimize the decision making process.
Throughout this research, multiple algorithms were created that use concepts from both control theory and machine learning, which provide new tools for robots to make better decisions. One algorithm enables a robot to learn how to optimally explore an unknown space, and autonomously decide when to explore for new information or exploit its current information. Another algorithm enables a robot to learn how to locomote with complex dynamics. These algorithms are evaluated both in simulation and on real robots. The results and analysis of these experiments are presented, which demonstrate the utility of the algorithms introduced in this work. Additionally, a new notion of "learnability'' is introduced to define and determine when a given dynamical system has the ability to gain knowledge to optimize a given objective function.

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ECE Ph.D. Dissertation Defenses

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Public
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Other/Miscellaneous
Keywords
graduate students, Phd Defense
Status
  • Created By: Daniela Staiculescu
  • Workflow Status: Published
  • Created On: Nov 23, 2015 - 3:14pm
  • Last Updated: Oct 7, 2016 - 10:14pm