CAREER Award To Help IC’s Byron Boots Bridge Gap Between Machine Learning, Engineering

Contact

David Mitchell

Communications Officer

david.mitchell@cc.gatech.edu

Sidebar Content
No sidebar content submitted.
Summaries

Summary Sentence:

Boots' research aims to develop new theory and algorithms that bridge the gap between machine learning and engineering approaches to robotics, which have traditionally been studied separately.

Full Summary:

No summary paragraph submitted.

Media
  • Byron Boots - Compressed Byron Boots - Compressed
    (image/jpeg)

School of Interactive Computing Assistant Professor Byron Boots earned a CAREER award from the National Science Foundation (NSF). The award is worth $479,527 and will be paid out over the course of five years.

The award was given in support of Boots’ research, a proposal for which was titled Designing Robots that Learn: Closing the Gap Between Machine Learning and Engineering. His research aims to develop new theory and algorithms that bridge the gap between machine learning and engineering approaches to robotics, which have traditionally been studied separately.

“While engineering uses knowledge of physics to provide interpretability, transparency, and guarantees about the reliability and robustness of engineered systems,” Boots said, “machine learning studies data and information, and provides guarantees that focus on the expressivity of models, computational cost, and sample efficiency of learning algorithms.”

Robots are most successful when interactions with their environment can be precisely defined. A notable example is manufacturing, where one of the keys to success is the ability to engineer the task environment to keep interactions simple, repeatable, and, therefore, predictable. However, as robots and their environments become more complex, accurate modeling becomes more difficult, which can lead to suboptimal or even dangerous behavior.

This presents a challenge: For robotics to advance, new approaches are needed for the complex scenarios where conventional modeling fails.

Boots’ research will combine the strengths of hand-crafted, physics-based models with machine learning algorithms, which allow computers to infer predictive models directly from data collected during a robot’s operation. A combined approach, Boots said, will better position engineers to design robots that can rapidly learn to operate in less-structured, real-world environments by observing their own behavior.

The NSF Faculty Early Career Development Program is a Foundation-wide activity that offers the NSF’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education.

Boots came to Georgia Tech in 2014 after spending time as a post-doc in the Robotics and State Estimation Lab at the University of Washington. He received his Ph.D. from Carnegie Mellon University.

Related Links

Additional Information

Groups

College of Computing, ML@GT, School of Interactive Computing

Categories
No categories were selected.
Related Core Research Areas
Robotics
Newsroom Topics
No newsroom topics were selected.
Keywords
byron boots, School of Interactive Computing, machine learning, robotics, robots, engineering, College of Computing, National Science Foundation, National Science Foundation CAREER Award
Status
  • Created By: David Mitchell
  • Workflow Status: Published
  • Created On: Apr 11, 2018 - 1:04pm
  • Last Updated: Apr 11, 2018 - 1:04pm