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DARPA's TIAMAT (Transfer from Imprecise and Abstract Models to Autonomous Technologies) program develops sim-to-real transfer techniques that train autonomous systems in low-fidelity, abstract simulations and reliably deploy them in unpredictable physical environments. Research targets foundations for rapid sim-to-real generalization, robustness to physics-model error, and transfer learning across morphology and sensor configurations.
The program is structured as two 18-month phases and supports applications including ground autonomy, aerial autonomy, and robotic manipulation in adversarial, novel, or contested settings. Awards have included University of Central Florida ($1. 2M) and other academic and industry performers.
TIAMAT is a core DARPA investment in foundational autonomy R&D.
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Or search similar grants →According to the current listing, eligibility includes: Open to U.S. and certain non-U.S. universities, FFRDCs, industry, small businesses, and non-profit research organizations. Performers are selected through proposal solicitations under DARPA I2O office-wide BAAs or program-specific announcements. Confirm the full requirements in the official notice before applying.
The current listing shows multi-million dollar awards. Performer awards have ranged from approximately USD 1,200,000 (e.g., UCF) to USD 5,000,000 across two 18-month phases. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for DARPA TIAMAT Transfer from Imprecise and Abstract Models to Autonomous Technologies for Sim-to-Real AI in Unpredictable Environments are due December 31, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
DARPA TIAMAT Transfer from Imprecise and Abstract Models to Autonomous Technologies for Sim-to-Real AI in Unpredictable Environments is funded by Defense Advanced Research Projects Agency (DARPA) Information Innovation Office (I2O). Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
AFRL's Geospatial Intelligence Processing and Exploitation (GeoPEX) BAA seeks white papers for research, development, integration, test and evaluation of technologies and techniques to provide geospatial intelligence (GEOINT) in all its forms and from whatever source — imagery, imagery intelligence, or geospatial data and information. The open, two-step BAA prioritizes AI-enabled sensor fusion, machine learning for imagery exploitation, multi-source data integration, autonomous geospatial reasoning, and intelligent processing pipelines. Total funding approximately $99.9M with individual awards typically $250K to $10M over up to 24 months. White papers accepted on a rolling basis until September 30, 2026; formal proposals are by invitation only.
The U.S. Army's SBIR/STTR Artificial Intelligence/Machine Learning Open Topic provides non-dilutive funding to small businesses developing AI and machine learning solutions for defense applications such as supply chain management, logistics coordination, target identification, and modeling and simulation. Awards follow the standard SBIR/STTR structure: Phase I establishes technical feasibility (typically up to about $250,000) and Phase II funds development and demonstration (typically up to about $2,000,000), with Phase III for commercialization. Open-topic solicitations accept proposals on a periodic basis.
DARPA's REMA (Rapid Experimental Missionized Autonomy) program enhances commercially available and stock military drones by adding an autonomy subsystem that increases the drone's capability and renders ineffective adversaries' electromagnetic countermeasures that disrupt operator-drone communication. The single-phase program develops a drone-autonomy adapter interface and mission-specific autonomy software, utilizing accelerating development spirals for tactical deployment. Focus areas include onboard AI inference for navigation, target recognition, and mission continuation in GPS-denied and jammed environments; foundation models for tactical autonomy; and modular autonomy stacks compatible with diverse commercial drone platforms. Managed by DARPA TTO under PM Phillip Smith.
DARPA's DICE program (HR001126S0010) wants the theory and algorithms for a self-organizing collective of AI agents that stays on mission in contested environments with no central controller. It is a 36-month, simulation-only, three-technical-area BAA due August 25, 2026 — and it rewards a rare blend of distributed-systems theory and frontier-model inference control that almost no single lab has under one roof. Here is what DICE is really asking for, who is positioned to win, and how to build a team that can.
Read articleDARPA transferred its first autonomous-ready H-60Mx Black Hawk to the Army on March 20, capping a decade of ALIAS research. Now the same technology underpins an SBIR XL opportunity for small businesses building wildfire autonomy.
Read articleDARPA's FY26 SBIR Release 5 pre-releases August 5, 2026, opens August 26, and closes September 23 — a four-week window. The Department of War now pre-releases SBIR/STTR topics the first Wednesday of every month, turning DARPA solicitations into a predictable annual cadence you can plan around. The most valuable and least-used part is the three-week pre-release period, when you can talk directly to the topic authors before proposals open. Here is how to turn the monthly rhythm and the pre-release window into a real competitive edge.
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