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AI for Physical Systems

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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.

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:Ìý

• 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 bio-hybrid components
• Platforms to support the next generation of robotic manufacturing based on mass-customizability and intuitive learning
• 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
• Scientific discovery

Internal Application Requirements

1. Project Summary (2 pages maximum) including:Ìý
   1. Transformative Mission: How will your proposed Mission create or reshape an entire field of scientific research or sector of technology?
   2. Platform Technology: How will your proposed Mission develop early-stage platform technologies that enable breakthroughs and accelerate entirely new forms of AI integration in physical systems including advanced sensors, robotics, embodied systems, human-robot interfaces or cyber-physical systems?
   3. Unmet Need: How will your proposed Mission develop a technology not presently supported by existing R&D investments by the U.S. public or private sector?
   4. Team & Organizational Structure: What is your anticipated team structure, and how will the proposed X-Lab address research challenges that traditional university and industry labs cannot easily address?
2. List of team members and affiliationsÌý
3. Internal Teaming Abstract (optional): Please provide a brief project abstract (3-4 sentences) for teaming purposes. By submitting this abstract, you give RIO permission to share your name and abstract with potential ÃÛÌÒ´«Ã½Æƽâ°æÏÂÔØ teams as part of the internal competition process, including if your proposal is not selected.
4. Anticipated Development Needs (optional): Briefly identify any anticipated needs related to team development, expertise, partnerships, organizational capacity, or other resources that may be important to developing a competitive proposal.

Deadlines

• CU Internal Deadline: October 19, 2026 @ 11:59 p.m. MT
• NSF Written Proposal Deadline: January 7, 2027 @ 3 p.m. MT

Request for Information (RFI): Identifying the Next Generation of NSF X-Labs Topics

On September 16, NSF issued an RFI titled to obtain public input on scientific and technological opportunities that could become future topics for the NSF X-Labs Initiative. NSF seeks information on high-impact platform technology opportunities, critical technical bottlenecks, enabling infrastructure, potential partnerships, and conditions under which dedicated X-Labs teams could accelerate scientific and technological breakthroughs. Responses are due by October 30, 2026.