The
2027 National Science Foundation (NSF) X-Labs Artificial Intelligence (AI) for Physical Systems is accepting proposals targeting early-stage platform technologies that enable breakthroughs to accelerate entirely new forms of AI integration in physical systems including advanced sensors, robotics, embodied systems, human-robot interfaces, or cyber-physical systems. Examples of relevant Missions include, but are not limited to:
- Technologies to enable new forms of distributed learning and swarming;
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Adaptive behavior in the absence of connectivity;
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Novel algorithms for sensor integration into digital twin or cyber-physical simulations;
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New capabilities in edge AI for physical systems;
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New modalities for AI control of emerging robotics such as bio-inspired or biohybrid components;
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Platforms to support the next generation of robotic manufacturing based on mass-customizability and intuitive learning; and
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Breakthroughs in the development of training data that, together with other advances, prepare physical AI systems for real-world variability including interactions with human counterparts.
The resultant innovative platform technologies should benefit a broad range of application domains critical to U.S. competitiveness and security, which may include healthcare, national defense, emergency response, advanced manufacturing, and scientific discovery.
An NSF X-Labs Mission in this Topic must be transformative, accelerating breakthrough R&D in physical AI towards creating or reshaping new lines of research and technologies. Successful teams will develop platform technologies, overcome technical barriers facing AI for physical systems, demonstrate measurable impact on the U.S. science and technology landscape, and position their technologies for widespread use and investment.
Below are examples of challenges not considered in scope for this Topic:
For more information, interested investigators can attend the Q&A webinar for this NSF X-Lab topic on Wednesday, October 14 at 1pm. Register here.
FUNDING INFORMATION: Up to $1.5M for Phase 0 and up to $50M per year for Phase 1.
ELIGIBILITY: Eligible X-Labs must be institutionally independent organizational structures. Thus, to be eligible for an NSF X-Labs Phase 1 award, teams are expected to operate with substantial independence in decision making, staffing, funding, partnerships, and day-to-day operations. Prior to and/or during Phase 0, NSF anticipates that some teams may not yet have established an independent governance structure required of a Phase 1 NSF X-Lab. In such cases, NSF expects to issue a Phase 0 award to a lead organization that will act as a host for the team.
Consult the X-Labs Autonomy Factor Assessment in the September 16 Amendment (section 6.1.1) available on the opportunity website.
INTERNAL SELECTION PROCESS: BU may submit up to two proposals as a lead organization. Interested applicants should submit the following materials via InfoReady Review by Oct. 22.
- Team Mission Summary (2 pages max) describing the team’s mission, how the mission connects with this Topic, and the significance of the proposed transformative approach.
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List of Collaborators.
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Up-to-date CV for the PI or Co-PI(s).
As necessary, a faculty review committee will review internal applications and select the institutional nominee(s).
DEADLINES:
- Internal Materials Due: Thursday, October 22, 2026 by 11:59 pm ET
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Anticipated Notification Date: Friday, November 6, 2026
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External Materials Due: Thursday, January 7, 2027
PREVIEWED TOPICS TO BE ANNOUNCED LATER THIS FALL
From Sequence to Function: This topic will enable advances in biological engineering by linking protein sequence and structure to biological function. NSF X-Labs in this topic will enable these advances by “creating platform technologies that enable the intentional design of individual proteins or entire genomes whose function is predictable and/or controllable.”
Computation at the Limit of Physics: This topic focuses on narrowing the gap between energy efficiency of conventional Complementary Metal-Oxide-Semiconductor-based computing and the inherent physical limits, to maintain the computational efficiency and capability required to drive innovation.
In requesting to be considered for this limited submission funding opportunity, you are making a commitment, if selected, to submit your proposal to the sponsor in a timely manner and to Sponsored Programs in accordance with the Proposal Submission Policy
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