ARO - 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 learning perspective, 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.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 08, 2019
- Source ID
- W911NF1910273
Entities
People
- Ali Jadbabaie
Organizations
- Army Contracting Command
- Massachusetts Institute of Technology
- United States Army