ONR - Learning for Dynamics and Control Conference
Abstract
Over the past few years, machine learning has had tremendous impact in numerous areas such as computer vision and language translation. Over the next decade, the biggest generator of data is expected to be devices which sense and control the physical world. This explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning, model-based dynamical systems, and control and decision theory. While control theory has been firmly rooted in tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking of the foundations for our discipline. From a machine learningperspective, one of the main challenges going forward is to go beyond pattern recognition and address problems in data driven control and decision making as well as learningbased- optimization of dynamical processes.~We propose a two-day inaugural conference on the interface between learning, dynamics and control to take place on May 30-31 at MIT. Long term we would like to sustain this event as an annual conference where best results on this emerging interface are presented. We aim to create a new community of people that think rigorously across the disciplines, ask novel fundamental questions, and develop the foundations of this new scientific area. We feel that our Proposed effort will result in a very influential annual conference that can attract a lot of attention from numerous scientific communities.~Value Proposition and Rationale: The goal of this proposal is the creation of a scientific forum that brings together pioneers and state of the art research in the areas of control systems, optimization, machine learning, and related disciplines in order to create a prestigious annual conference that defines the state-of-the-art in Learning for Dynamical and Control Systems. An elite conference on this topic can have tremendous impact not only scientifically by bridging two distant areas but also from a community perspective thatnurtures a growing number of junior researchers working on this emerging interface. An elite conference across control, optimization and learning will provide a natural and nurturing home for the professional development for students, postdocs, and junior faculty. Furthermore, the topics covered in this proposal are of paramount importance to advancing Navy~s capabilities on AI, Machine Learning, and Autonomy.~
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 23, 2019
- Source ID
- N000141912370
Entities
People
- Ali Jadbabaie
Organizations
- Massachusetts Institute of Technology
- Office of Naval Research
- United States Navy