Category: Opportunity
DoD Communities Of Interest: Artificial Intelligence
Subject: ARL Research topic Artificial Intelligence (AI) and Machine Learning (ML)
Due Date: Mar 15, 2022 05:00 pm EDT
Government Organization: The Army Research Laboratory (ARL)
Description:
The U.S. Army posted a special notice for AI/ML Research for Expeditionary Maneuver and Air/Ground Reconnaissance research and development. Responses are due by 5:00 p.m. Eastern on March 15.
The Army Research Laboratory (ARL) is planning to consider proposed research and development solutions submitted under the ARL Broad Agency Announcement (BAA) for Basic and Applied Scientific Research topic Artificial Intelligence (AI) and Machine Learning (ML) (section II.A.4.k.) in support of new technologies and translational research-based approaches in two (2) areas: (1) Expeditionary Maneuver and (2) Air/Ground Reconnaissance.
- AI/ML Research for Expeditionary Maneuver
Problem: How do Intelligent Systems reason about, interact with, navigate through, and manipulate a dynamic environment to achieve complex Army-relevant actions for the future multi-domain battlespace?
Specific areas of interest are:
Methods to learn physics abstractions of objects from observations and to learn and validate models through interaction with the environment.
Ability to infer, learn, and model the dynamics of complex objects, including humans, to include predicted outcomes of their physical interactions with the intelligent system.
Through physical probing and learned experiences, enhanced physical scene understanding enables maneuver and interaction planning for complex and resilient maneuvers and behaviors.
- AI/ML Research for Air/Ground Reconnaissance
Problem: How do diverse, embodied agents collectively sense, infer, reason, plan, and execute in collaboration with Army warfighters and the face of a peer adversary? Additionally, how do these agents extend the reach, situational awareness, and operational effectiveness of Army Intelligent System/Soldier teams against dynamic threats in complex and contested environments?
Specific areas of interest are:
Intelligent systems that can learn and adapt to dynamic changes in the environment
Self-organizing teams based on mission goals and available systems that can compose and share representations of the world to coordinate on the local and global level
Ability to learn and interact with humans at multiple levels
Systems that learn perception-action-communication loops to adapt and modify behaviors as the mission demands and the communication constraints and the data allows
Innovative research solutions are sought in the areas described above under section II.A.4.k. of the ARL BAA. It is anticipated that initial cooperative agreements resulting from this Special Notice will be made in early FY23. Awards are expected to have a two (2) year period of performance, and, depending on proposals submitted, an additional option year may be included in the final award. To be considered for an invitation to submit a proposal, applicants must comply with the submission instructions included in the ARL BAA.
Website: https://sam.gov/opp/02d07e3efd3f4f6c8be4aa2765932eb9/view
Questions or assistance, contact:
North Carolina Defense Technology Transition Office (DEFTECH)
Dennis Lewis
lewisd@ncmbc.us
703-217-3127
Bob Burton
burtonr@ncmbc.us
910-824-9609
North Carolina Defense Technology Transition Office | PO Box 1748, Fayetteville, NC 28303