NSF X-Labs Initiative – AI for Physical Systems
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Limiting Language
An eligible organization can submit a maximum of two Written Proposals per Topic Announcement for Phase 0 as a lead organization. Senior/Key Personnel may be listed on a maximum of one Written Proposal per Topic Announcement.
Topic Description
Decades of progress in AI algorithms, robotics, sensing, and embedded computing, combined with accelerating industry investment, have positioned the U.S. to redefine how intelligent systems interact with the physical world. While digital AI has advanced rapidly, the next frontier requires developing intelligent, adaptive, and scalable systems capable of perceiving, acting and learning directly within complex physical environments with adaptive human-robot interaction.
Achieving this vision demands innovations in foundational platform technologies to support situational adaptability, continuous learning, and right-sized simulation-to-reality systems.
Imagine heterogeneous autonomous swarms in military and disaster triage environments capable of high-stakes collective decision making in collaboration with human counterparts and the situational adaptability to perform complex locomotion, sensing, and targeted delivery tasks. Or bio-inspired robotics that operate with adaptive sensing for reflexive sensor input control, neuromorphic cognitive efficiency, and self-healing materials and behaviors. These advances demand significant, continual, and cross-disciplinary ecosystems to save lives, and catalyze U.S. technological and economic innovation.
NSF X-Labs teams will target 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; adaptive behavior in the absence of connectivity; novel algorithms for sensor integration into digital twin or cyber-physical simulations; new capabilities in edge AI for physical systems; new modalities for AI control of emerging robotics such as bio-inspired or biohybrid components; platforms to support the next generation of robotic manufacturing based on mass-customizability and intuitive learning; and 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. Teams proposing to this Topic must justify that their proposed Mission will develop a technology not presently supported by existing R&D investments by the U.S. public or private sector.
Examples of challenges not considered in scope for this Topic include computational or software solutions without practical integration into a physical system, integration of current technologies without significant advancement of the state-of-the-art, development of technologies where the impact is narrow and not widely deployable, fundamental research without potential for application in platform technologies, incremental advancement of the state-of-the-art, or advancement of technologies that are already appropriately developed to the point of full-scale commercialization.