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Intelligent, Fast Reinforcement Learning for ISR Tasking (IFRIT)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-22-C-0180
Agency Tracking Number: N21B-T021-0072
Amount: $139,987.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N21B-T021
Solicitation Number: 21.B
Timeline
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-01-11
Award End Date (Contract End Date): 2022-07-27
Small Business Information
500 West Cummings Park Suite 3000
Woburn, MA 01801-1111
United States
DUNS: 859244204
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Reece DeLong
 (781) 933-5355
 Reece.DeLong@ssci.com
Business Contact
 Lora Loyall
Phone: (781) 933-5355
Email: contracts@ssci.com
Research Institution
 Massachusetts Institute of Technology
 Stacey Sullaway
 
77 Massachusetts Avenue NE18-901
Cambridge, MA 02139-4307
United States

 (617) 324-7210
 Nonprofit College or University
Abstract

Scientific Systems Company, Inc. (SSCI) in conjunction with our academic partners at MIT, propose the Intelligent, Fast Reinforcement Learning for ISR Tasking (IFRIT) system, to provide sophisticated autonomous end to end ISR mission execution capability. The IFRIT framework will enable real time adaptive mission planning that responds effectively to dynamic environments, leveraging reinforcement learning (RL) and Macro-Action Partially Observable Markov Decision Process (Mac-POMDP) decision techniques. Leveraging these RL techniques in conjunction with macro-actions integrating SSCI’s CMA behavior software for EO/IR area search and RF emitter geolocation, provides flight tested genetic algorithms and other combinatorial techniques for online, real-time multi-objective optimization, fusing live sensors, operator inputs, and comms quality data. A mission planner utilizing RL, Mac-POMDP decision techniques and CMA behaviors provides a mission planner that performs effective planning of macro-actions for an ISR Mission. This mission planner augmented with modern embedded CPU/GPU combinations, including GPUs for deep network usage computation to assist with rapid RL policy approximations makes IFRIT an effective mechanism for mission planning and dynamic replanning as external factors present in a real time dynamic environment. Incorporating a mission monitor for output of this mission planner provides the IFRIT system with models to ensure desirable behavior and tune the level of autonomous replanning desired by the operator.

* Information listed above is at the time of submission. *

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