NSF X-Labs Physical AI Out-of-Scope Annex

NSF X‑Labs AI for Physical Systems Annex – Out‑of‑Scope Purpose: Clarifies topics that are likely out-of-scope and nonresponsive to the Artificial Intelligence (AI) for Physical Systems Topic and provides a quick screening checklist for applicants. The examples provided in this Annex are illustrative and do not establish additional evaluation criteria. Proposals will be evaluated using the selection criteria stated in Sections 11 and 13 of the NSF-OTASO-FY26XLabsInitiative or latest amendment. Physical AI definition: Intelligent, adaptive, scalable AI systems embodied in or interfacing with the physical world, which may include (but not be limited to): robots and cyber-physical systems; implantable, ingestible, or wearable AI devices; neural or bio-integrated platforms; and built-environment/infrastructure-scale systems. Out‑of‑scope examples
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Mission fit & platform scope: Narrow, single product efforts that do not generalize into reusable platform technology across domains; integration “glue” around existing tech that yields only incremental improvements; fundamental theory or algorithms without hardware or wetware-in-the-loop validation; proposals primarily focus on testbeds, data hubs, or cloud infrastructure as the mission rather than enablers within a platform advancing Physical AI.
Technical content (what the platform is/does): Simulation- or digital-twin-only work lacking plans to demonstrate sim-to-real transfer; large language model (LLM) wrappers or policy tweaks that control commercial off-the-shelf robots without new embodied capabilities in perception-action-learning; narrow device-layer add-ons, instrumentation, User Interface (UI) or dashboards, teleoperation apps, single-sensor improvements, or generic wearables that do not produce closed-loop autonomy/adaptability; biological cataloging or data-lake plays that do not yield novel embodied control or controlled training datasets tied to platform validation; speculative bio-AI (e.g., unconstrained synthetic neural implants) without feasible in-vitro/in-vivo validation, safety mitigations, and regulatory and ethical oversight; single-indication medical devices primarily seeking trial or market scale-up funding without overcoming a fundamental embodied-AI barrier; services or tooling-only efforts that do not deliver predictable, controllable embodied behavior in biological/physical systems.
Maturity boundaries: Not aligned to early-stage platform development [~Technology Readiness Levels