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DARPA's TIAMAT (Transfer from Imprecise and Abstract Models to Autonomous Technologies) program tackles the sim-to-real transfer problem in robotics and autonomous systems by training autonomous systems in low-fidelity simulations and then transferring that learned behavior into the real world.
The program addresses one of the central barriers to deploying autonomous ground vehicles and other robotic systems at military scale: the cost and brittleness of training in high-fidelity simulation or live environments. TIAMAT is structured around two 18-month phases that move from foundational sim-to-real algorithm research to integrated demonstrations on physical platforms.
The program supports a small number of high-performing teams of universities, defense contractors, and small businesses developing the next generation of robust autonomy for ground, aerial, and maritime applications.
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Or search similar grants →According to the current listing, eligibility includes: Universities, defense prime contractors, small businesses, FFRDCs, and non-traditional defense performers eligible under DARPA Broad Agency Announcements. Strong teams typically combine machine learning research depth with platform integration capability. Foreign participation is restricted per standard DARPA terms. Confirm the full requirements in the official notice before applying.
The current listing shows DARPA TIAMAT performer awards run as multimillion-dollar contracts across two 18-month phases. Documented academic awards include approximately $1,200,000 to the University of Central Florida and multimillion-dollar grants to Johns Hopkins University Applied Physics Laboratory. Verify award ceilings, matching requirements, and allowable costs in the official notice.
DARPA TIAMAT Transfer from Imprecise and Abstract Models to Autonomous Technologies for Sim-to-Real Autonomy is funded by Defense Advanced Research Projects Agency (DARPA). 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 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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