The Case for Physical AI Safety

Background

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 RFMs at the Conference on Robot Learning (CoRL’25)—one of the major robotics research venues.

Today, 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.

Get Involved

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.

Support — Donate to fund this work, or get in touch about supporting PAISI.

Thanks

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 $18.8B to build these AI models (Table 1) 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 to physically interact with the world around them introduces serious new risks compared to non-embodied AI. Since there are very strong economic incentives to widely deploy RFMs, these risks indicate that physical AI safety will become very important in the near future.

While a rich safety literature exists for narrow task-based robotics, very few techniques exist to interpret, align, and control RFMs: physical AI safety research is neglected. However, there are many promising approaches in both classic robotics and in non-physical foundation models that could be adapted to RFMs, suggesting that this research is also tractable.

To mitigate plausible near- and long-term risks, we believe that more people should be researching methods to interpret, align, and control RFMs.

To catalyze this future, we are launching The Physical AI Safety Institute. A capacity-building organization at heart, we're aiming to grow the community developing physical AI safety solutions through fellowships, workshops, and open online learning.

Introduction

Company

Model/Humanoid

Valuation

Raised

RFM Oriented

Skild AI

Skild Brain

$14B

$1.8-2B

Physical Intelligence

series [1]

$5.6B

$1.07B

World Labs

Marble

~$5B

$1.23B

Others [2]

~$4B

Subtotal Raised

~$7.9B

Humanoid oriented

Figure AI

Helix / Figure 03

$39B

$1.9B

1X Technologies

1X World Model / NEO

~$10B [3]

$0.126B

NEURA Robotics

Isaac GR00T / 4NE-1

~$7B

$1.7B

Others [2]

~$7.1B

Subtotal Raised

~10.8B

Table 1: Physical AI funding as of mid-2026

It's an exciting time for robotics. Autonomous vehicles shuttle passengers across cities on demand, quadruped and humanoid robots walk on uneven ground with remarkable robustness [4], and robot arms with dexterous hands complete increasingly complex tasks, including using scissors and preparing loose-leaf tea [5]. But the development that has really upended the robotics community is the arrival of robot foundation models (RFMs) like Physical Intelligence's and NVIDIA's GR00T N1. Powered by LLM-like architectures, these models seem poised to finally bring robots into offices and homes. Their development is backed by a rapidly increasing amount of investment: startups developing RFMs have raised at least $7.9B, while those focused on general-purpose humanoid robots have raised at least another $10.8B (Table 1).

What's the big deal about RFMs? For decades, progress in robotics meant progress on narrow, special-purpose action policies: a policy that stitches wounds [6], a policy that climbs stairs [7]. When natural language and computer vision research was upended by general-purpose foundation models that could accomplish many different tasks, robotics remained unperturbed. Naive attempts to hook up LLMs to control robots produced uninspiring results due to challenges with spatial and physical understanding, in part due to lack of training data. Since late 2023, this has no longer been the case. A number of startups and established AI companies began spending billions of dollars on RFM development, investing significant resources in collecting thousands of hours of robotics data and iteratively resolving the engineering challenges of putting a foundation model in control of a robot. A race to put general-purpose humanoid robots in homes, offices, and warehouses is now well under way, with at least two companies promising to ship units to homes this year [8]. Whether or not the technology is actually quite this mature yet, the vision is clear: robots that can be dropped in a new environment and asked to do various long-horizon tasks in ordinary language.

Why does this matter for safety? LLM agents can take actions in digital space to manage email or a calendar; RFMs give robot agents the direct ability to take physical actions in the real world. Today, an AI system that wants something harmful to happen in the physical world often faces a bottleneck: it must convince a human to act on its behalf. In a case now in litigation, Google's Gemini allegedly gave someone the address of a real storage unit at Miami International Airport and instructed him to stage a "catastrophic accident'" eliminating a truck, its digital records, and the witnesses [9]. Fortunately, the person gave up waiting for the truck to arrive and went home. If this story were about an embodied RFM instead of a disembodied LLM, the human bottleneck would not have stopped the AI from inflicting real-world harm [10].

Unfortunately, physical AI safety risks do not stop there. A plausible set of AI harms associated with physical embodiment include:

Pursuing harmful goals instilled by a malicious actor, e.g. because of jailbreaking, hacking, or altering the RFM to be more dangerous.

Pursuing harmful goals because of lack of alignment with human preferences and values.

Accidental physical harm due to lack of robustness in unusual or unevaluated scenarios.

Human emotional attachment and dependency due to physical embodiment.

Compromising privacy due to sensitive information captured by continuously operated mobile sensors.

We believe physical AI is a blind spot in AI safety research. Most current work on AI safety assumes that risky AI looks similar to today's LLMs, where potential harms arise from cyberspace actions such as hacking computer systems or giving harmful instructions to humans. Furthermore, it assumes that AI systems will continue to have the same basic architecture and training regimes of current LLMs. Physical AI breaks both of these assumptions.

In…