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arxiv: 2606.23754 · v1 · pith:CTPPDV4Anew · submitted 2026-06-22 · 💻 cs.RO · cs.LG

Verifiable Foundation Models for Robot Safety

Davide Corsi , Kyungmin Kim , Roy Fox This is my paper

Pith reviewed 2026-06-26 08:52 UTC · model grok-4.3

classification 💻 cs.RO cs.LG
keywords foundation modelsrobot safetyformal verificationmodular architecturepolicy decompositionsim-to-real transferassured robot learning
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The pith

A modular split separates a large expressive controller from a small verifiable safety module in foundation-model robot policies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Foundation models give robots strong perception and task reasoning but resist formal safety analysis because of their size and opacity. The paper proposes a decomposition that keeps the large Controller for high-dimensional inputs and reasoning while routing final actions through a small Safety module that sees only low-dimensional safety sensors plus a bounded embedding. Safety properties expressible on those sensors, such as collision avoidance, can then be checked with existing verification tools. Experiments across simulated domains with multiple backbones, including off-the-shelf vision-language-action models, plus one physical-robot transfer, show that task capability is retained.

Core claim

FEARL decomposes the policy into a Controller C that performs high-dimensional perception and task reasoning and a Safety module S that receives low-dimensional safety observations together with a bounded context embedding from C and outputs the final action; because many safety requirements can be stated over the safety observations, formal verification applies to S alone while the Controller keeps its full expressive power, as confirmed by task success in three simulated domains and successful transfer to a physical robot.

What carries the argument

The modular architectural decomposition into Controller and Safety module with a low-dimensional safety interface.

If this is right

  • Formal verification of safety properties becomes tractable without analyzing the full foundation-model backbone.
  • The decomposed policy continues to solve diverse tasks when the Controller uses pretrained vision-language-action models or other backbones.
  • The low-dimensional safety interface supports sim-to-real transfer on at least one evaluated task.
  • Multiple training procedures for the Controller remain compatible with the Safety module.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If a safety requirement inherently needs high-dimensional scene information, the low-dimensional Safety module may be insufficient to capture it.
  • The same separation could be tested on other safety-critical control problems where dedicated low-dimensional sensors already exist.
  • Standardizing the bounded context embedding passed from Controller to Safety module might allow reusable Safety modules across different foundation-model backbones.

Load-bearing premise

Robot safety requirements such as collision avoidance and workspace limits can be expressed and enforced using only the low-dimensional observations available to the Safety module.

What would settle it

A case in which the Safety module passes formal verification yet the physical robot still violates a safety constraint during execution, or in which the decomposed policy fails to reach task performance levels achieved by an undecomposed foundation-model baseline.

Figures

Figures reproduced from arXiv: 2606.23754 by Davide Corsi, Kyungmin Kim, Roy Fox.

Figure 1
Figure 1. Figure 1: Overview of the FEARL C/S decompo￾sition: Controller (C) processes high-dimensional task inputs into low-dimensional context, while Safety module (S) uses this context and low￾dimensional safety signals to produce the final, verifiable action. Second, and more subtly, the replacement ac￾tion returned by the shield gives no guarantee that it is the best alternative with respect to the original task objectiv… view at source ↗
Figure 2
Figure 2. Figure 2: Three environments across different robot platforms. 5 Experiments We evaluate FEARL across domains that vary in task complexity, sensing modality, robot form fac￾tor, and Module C instantiation. The experiments are organized around two central objectives. First, we assess whether the C/S decomposition supports effective policy learning: despite delegating all safety-critical decisions to the small Module … view at source ↗
Figure 3
Figure 3. Figure 3: Real-world indoor navi￾gation setup. In real-world rollouts over 18 episodes, the shielded policy achieves a 61.1% success rate with zero collisions, while dis￾abling the shield results in the same success rate but 27.8% collision rate. The lower success rate compared to simulation is consistent with expected sim-to-real distribution shift in the task-level inputs, such as localization drift and sensor noi… view at source ↗
read the original abstract

Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes these models opaque and difficult to analyze formally, rendering them intractable for existing verification tools. In this paper, we present FEARL (Foundation-Enabled Assured Robot Learning), a framework that addresses this tension through a modular architectural decomposition. FEARL separates the policy into a large Controller (C) responsible for high-dimensional perception and task reasoning, and a small Safety module (S) that receives low-dimensional observations from dedicated safety sensors together with a bounded context embedding from C and produces the final action. Since many robot safety requirements, such as collision avoidance and workspace boundary constraints, can be expressed over these safety sensor observations, formal verification can be applied to S rather than to the full foundation-model backbone. This makes formal analysis tractable with existing tools while preserving the Controller's expressive power for task reasoning. To show that the decomposed policy remains capable of solving diverse tasks, we evaluate FEARL on three simulated robotic domains using multiple Controller backbones and training procedures, including pretrained off-the-shelf vision-language-action models. We further transfer the learned policy from one of our simulated tasks to a physical robot, suggesting that the low-dimensional safety interface supports practical sim-to-real transfer.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper introduces FEARL, a modular architecture for robot control that decomposes the policy into a large Controller module handling high-dimensional perception and task reasoning using foundation models, and a smaller Safety module that operates on low-dimensional safety sensor observations augmented by a bounded context embedding from the Controller. The framework claims to enable formal verification of safety properties on the Safety module while maintaining the Controller's expressiveness, supported by evaluations across three simulated robotic domains with various backbones including pretrained vision-language-action models, and a sim-to-real transfer to a physical robot.

Significance. If the assumption that key safety constraints can be adequately captured and enforced via the low-dimensional interface holds, this work could provide a practical pathway to combining the capabilities of foundation models with formal safety guarantees in robotics, addressing a key barrier to their deployment in real-world settings. The inclusion of sim-to-real transfer and multiple backbone evaluations strengthens the practical relevance.

major comments (3)
  1. [Abstract] Abstract, paragraph 3: The central claim that formal verification can be applied to S rests on the assertion that 'many robot safety requirements, such as collision avoidance and workspace boundary constraints, can be expressed over these safety sensor observations.' No argument, concrete examples, or evidence is supplied showing that the chosen low-dimensional observations are sufficient for the evaluated tasks, particularly when safety may require semantic distinctions available only to C.
  2. [Abstract] Abstract: Despite the framework's emphasis on making formal analysis tractable, the manuscript reports no quantitative verification results, proof sketches, or description of verification tools applied to S. Claims therefore rest entirely on qualitative policy success and transfer, leaving the primary promised benefit undemonstrated.
  3. [Abstract] Abstract: No ablation or analysis is provided on the effect of the bounded context embedding from C on S's safety enforcement capability or on the tightness of the resulting verification bounds, which is load-bearing for assessing whether the decomposition preserves both verifiability and task performance.
minor comments (1)
  1. The evaluation description could more explicitly separate results on policy capability from any verification-related metrics, even if the latter are absent.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each of the three major comments point-by-point below, indicating planned revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract, paragraph 3: The central claim that formal verification can be applied to S rests on the assertion that 'many robot safety requirements, such as collision avoidance and workspace boundary constraints, can be expressed over these safety sensor observations.' No argument, concrete examples, or evidence is supplied showing that the chosen low-dimensional observations are sufficient for the evaluated tasks, particularly when safety may require semantic distinctions available only to C.

    Authors: We agree that more explicit justification is warranted. In the revised manuscript, we will augment the abstract and add a subsection detailing how the safety sensors (e.g., joint encoders, distance sensors) suffice for the safety properties in our domains. We provide examples such as enforcing minimum distance to obstacles using raw sensor data in S, independent of semantic perception handled by C. This will be supported by task descriptions. revision: yes

  2. Referee: [Abstract] Abstract: Despite the framework's emphasis on making formal analysis tractable, the manuscript reports no quantitative verification results, proof sketches, or description of verification tools applied to S. Claims therefore rest entirely on qualitative policy success and transfer, leaving the primary promised benefit undemonstrated.

    Authors: This is a valid observation. While the paper establishes that S is small and thus verifiable in principle, we did not include explicit verification experiments. We will revise by adding a verification subsection that applies an off-the-shelf tool (e.g., a reachability analyzer) to S, providing quantitative results on verified properties and computation times for the simulated domains. revision: yes

  3. Referee: [Abstract] Abstract: No ablation or analysis is provided on the effect of the bounded context embedding from C on S's safety enforcement capability or on the tightness of the resulting verification bounds, which is load-bearing for assessing whether the decomposition preserves both verifiability and task performance.

    Authors: We concur that this analysis is important for validating the decomposition. The revision will incorporate an ablation study varying the context embedding (including a no-context baseline), evaluating impacts on safety metrics and verification complexity (e.g., state-space size). Results will be presented in the experiments section. revision: yes

Circularity Check

0 steps flagged

No circularity: framework defined by explicit architectural choice with independent empirical support

full rationale

The paper defines FEARL by construction as a decomposition into Controller C and Safety module S, with the latter receiving low-dimensional safety observations plus bounded context; the claim that formal verification applies to S follows directly from this interface choice and the stated assumption that many safety requirements (collision avoidance, workspace limits) can be expressed over those observations. No equations, fitted parameters, or self-citations are invoked to derive the safety guarantee from the framework's own outputs. The evaluations on simulated domains with multiple backbones and the sim-to-real transfer constitute independent content that does not reduce to the definition itself. This is a standard non-circular presentation of a proposed modular architecture.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that safety properties are expressible over low-dimensional sensor data; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Safety requirements such as collision avoidance can be expressed over low-dimensional safety sensor observations
    Stated in abstract paragraph 3 as the basis for applying formal verification to S

pith-pipeline@v0.9.1-grok · 5751 in / 1113 out tokens · 15884 ms · 2026-06-26T08:52:51.022388+00:00 · methodology

discussion (0)

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    Go toward the red goal with the digit 0

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    Move the agent to the blue destination marked 2. D Safety Specifications Safety properties are expressed as input-domain-conditioned output constraints on Module S: for a specified regionR ⊂ Sof the safety-sensor space, certain actions are declared unsafe and must not 13 be selected by Module S foranycontext embeddingz t ∈ Z. The formal statement iss t ∈ ...