# Modular Safety Guardrails Are Necessary for Foundation-Model-Enabled Robots in the Real World

arXiv: https://arxiv.org/abs/2602.04056 · HTML: https://mlsanity.com/paper/2602.04056/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2026.

## Built on by (most cited first, 7 of 7)

- [Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs](https://mlsanity.com/paper/2606.23938/) (2026)
- [Can Explicit Physical Feasibility Benefit VLA Learning? An Empirical Study](https://mlsanity.com/paper/2604.17896/) (2026)
- [How VLAs Fail Differently: Black-Box Action Monitoring Reveals Architecture-Specific Failure Signatures](https://mlsanity.com/paper/2605.28726/) (2026)
- [Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems](https://mlsanity.com/paper/2606.00090/) (2026)
- [The Boy Who Cried Wolf: Adversarial Misclassification of Safe Inputs as Unsafe in Multimodal Guardrails](https://mlsanity.com/paper/2608.01373/) (2026)
- [VLN on the Fly: An Onboard Vision-Language Navigation Stack for Aerial Robots](https://mlsanity.com/paper/2609.20191/) (2026)
- [Benchmarking Jailbreak Guardrails for Embodied Agents](https://mlsanity.com/paper/2610.06122/) (2026)

## Abstract (arXiv)

The integration of foundation models (FMs) into robotics has accelerated real-world deployment, while introducing new safety challenges arising from open-ended semantic reasoning and embodied physical action. These challenges require safety notions beyond physical constraint satisfaction. In this paper, we characterize FM-enabled robot safety along three dimensions: action safety (physical feasibility and constraint compliance), decision safety (semantic and contextual appropriateness), and human-centered safety (conformance to human intent, norms, and expectations). We argue that existing approaches, including static verification, monolithic controllers, and end-to-end learned policies, are insufficient in settings where tasks, environments, and human expectations are open-ended, long-tailed, and subject to adaptation over time. To address this gap, we propose modular safety guardrails, consisting of monitoring (evaluation) and intervention layers, as an architectural foundation for comprehensive safety across the autonomy stack. Beyond modularity, we highlight possible cross-layer co-design opportunities through representation alignment and conservatism allocation to enable faster, less conservative, and more effective safety enforcement. We call on the community to explore richer guardrail modules and principled co-design strategies to advance safe real-world physical AI deployment.