# LLM-Guided Safety Agent for Edge Robotics with an ISO-Compliant Perception-Compute-Control Architecture

arXiv: https://arxiv.org/abs/2604.20193 · HTML: https://mlsanity.com/paper/2604.20193/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 8 of 8)

- [Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos](https://mlsanity.com/paper/1903.03295/) (2019)
- [Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agents](https://mlsanity.com/paper/2309.09919/) (2023)
- [Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?](https://mlsanity.com/paper/2310.01831/) (2023)
- [Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards](https://mlsanity.com/paper/2410.15185/) (2024)
- [Grounding Generative Planners in Verifiable Logic: A Hybrid Architecture for Trustworthy Embodied AI](https://mlsanity.com/paper/2602.08373/) (2026)
- [IPAD: Industrial Process Anomaly Detection Dataset](https://mlsanity.com/paper/2404.15033/) (2024)
- [Executable Governance for AI: Translating Policies into Rules Using LLMs](https://mlsanity.com/paper/2512.04408/) (2025)
- [Pre-Execution Safety Gate & Task Safety Contracts for LLM-Controlled Robot Systems](https://mlsanity.com/paper/2604.05427/) (2026)

## Abstract (arXiv)

Ensuring functional safety in human-robot interaction is challenging because AI perception is inherently probabilistic, whereas industrial standards require deterministic behavior. We present an LLM-guided safety agent for edge robotics, built on an ISO-compliant low-latency perception-compute-control architecture. Our method translates natural-language safety regulations into executable predicates and deploys them through a redundant heterogeneous edge runtime. For fault-tolerant closed-loop execution under edge constraints, we adopt a symmetric dual-modular redundancy design with parallel independent execution for low-latency perception, computation, and control. We prototype the system on a dual-RK3588 platform and evaluate it in representative human-robot interaction scenarios. The results demonstrate a practical edge implementation path toward ISO 13849 Category 3 and PL d using cost-effective hardware, supporting practical deployment of safety-critical embodied AI.