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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.
The Army Applications Lab's DevX Autonomy open call seeks commercial autonomous and unmanned system solutions that can address Army capability gaps in maneuver, sustainment, and force protection. The solicitation accepts proposals on a rolling monthly basis through August 31, 2026, and is targeted at non-traditional defense vendors with autonomy software, AI perception systems, and unmanned ground or aerial platforms. AAL provides early access to Army end users for problem refinement, prototype evaluation, and transition planning. Awards are structured as Other Transaction Agreements, with awards typically between $500K and $2M and a pathway to follow-on Army programs of record. AAL is part of the U.S. Army Futures Command innovation portfolio.
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.
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.
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