The AI safety field has spent a decade building tools for systems trained to think and digitally act. The next decade will likely deliver widely deployed robot foundation models (RFMs): systems trained to think and physically act.
A year ago, we published the first mechanistic interpretability paper for steering RFMs at the Conference on Robot Learning (CoRL ’25)—one of the major robotics research venues.
This month, we’re launching the Physical AI Safety Institute (PAISI), a 501(c)(3) nonprofit dedicated to catalyzing the community that develops techniques to interpret, align, and control RFMs.
In the following white paper, we outline (1) why we think physical AI safety research is important, neglected, and tractable—and (2) PAISI's plan for building a community around this research.

Fellowship—Register your interest in an upcoming fellowship cohort.
Workshop—From August 12th, submit a paper or demo on RFM interpretability, alignment, or control to our Science of Physical AI Safety Workshop at CoRL ’26.

Thanks to Adam Gleave, Claire Tomlin, and Jean-Stanislas Denain for their thoughtful feedback during the development of this white paper.
We’re excited to launch with a seed grant from BlueDot Impact.

Executive Summary

Robotics is undergoing a paradigm shift: from modular 'perception-planning-control' pipelines to large general-reasoning robot foundation models (RFMs). The upcoming era is that of Physical AI.
RFMs take plain language instructions as input ("tidy the kitchen", "unload the truck") and produce physical navigation/manipulation commands to achieve the task. Companies have raised over $18.7B to build these AI models (Table 1)—some with the explicit goal of realizing embodied AGI. Dedicated funding for their safety, to our knowledge, rounds to zero. We are not aware of a capabilities-to-safety ratio this lopsided anywhere else in frontier AI.
Physical AI safety risks include malicious use, misalignment, accidental harm, emotional dependency, privacy violations, and broader societal risks such as inequality, malicious development, and power centralization. Physical embodiment is an AI risk amplifier, giving misaligned or misused foundation models direct causal access to the world and facilitating anthropomorphization.
Physical AI safety research is important, neglected, and tractable:
- The ability of RFMs

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