# SafePlan: Leveraging Formal Logic and Chain-of-Thought Reasoning for Enhanced Safety in LLM-based Robotic Task Planning

arXiv: https://arxiv.org/abs/2503.06892 · HTML: https://mlsanity.com/paper/2503.06892/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 6 of 6)

- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions](https://mlsanity.com/paper/2406.08824/) (2024)
- [SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models](https://mlsanity.com/paper/2409.19471/) (2024)
- [Robot Patrol: Using Crowdsourcing and Robotic Systems to Provide Indoor Navigation Guidance to The Visually Impaired](https://mlsanity.com/paper/2306.02843/) (2023)
- [Modeling and Evaluating Trust Dynamics in Multi-Human Multi-Robot Task Allocation](https://mlsanity.com/paper/2409.16009/) (2024)
- [How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach](https://mlsanity.com/paper/2412.11387/) (2024)

## Built on by (most cited first, 11 of 11)

- [VeriSafe Agent: Safeguarding Mobile GUI Agent via Logic-based Action Verification](https://mlsanity.com/paper/2503.18492/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses](https://mlsanity.com/paper/2605.02900/) (2026)
- [Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation](https://mlsanity.com/paper/2606.05660/) (2026)
- [A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM-Based Action Generation](https://mlsanity.com/paper/2603.08388/) (2026)
- [When Words Are Safe But Actions Kill: Probing Physical Jailbreak Beyond Textual Jailbreak in Hidden-State Risk Space](https://mlsanity.com/paper/2607.15218/) (2026)
- [GraphThink: Graph-Enhanced LLM Thinking for Long-Horizon Embodied Task Planning](https://mlsanity.com/paper/2608.07905/) (2026)
- [Security of Foundation-Model-Powered Embodied Agents: Attack Surfaces, Attacks, Defenses, and Evaluation](https://mlsanity.com/paper/2608.16843/) (2026)
- [MaCoPlanner: LLM-Assisted Manual-Compiled Task Planning with Proactive Safety Verification for Robotic Industrial Panel Operation](https://mlsanity.com/paper/2608.28300/) (2026)
- [Safe Task Planning with Long-Term Graph Memory for Embodied Agents](https://mlsanity.com/paper/2609.08444/) (2026)
- [SafeInferCom: Safe Inference-Time Compute via Verifier-Guided Mid-Generation Intervention for Robotic Task Planning](https://mlsanity.com/paper/2610.11223/) (2026)

## Abstract (arXiv)

Robotics researchers increasingly leverage large language models (LLM) in robotics systems, using them as interfaces to receive task commands, generate task plans, form team coalitions, and allocate tasks among multi-robot and human agents. However, despite their benefits, the growing adoption of LLM in robotics has raised several safety concerns, particularly regarding executing malicious or unsafe natural language prompts. In addition, ensuring that task plans, team formation, and task allocation outputs from LLMs are adequately examined, refined, or rejected is crucial for maintaining system integrity. In this paper, we introduce SafePlan, a multi-component framework that combines formal logic and chain-of-thought reasoners for enhancing the safety of LLM-based robotics systems. Using the components of SafePlan, including Prompt Sanity COT Reasoner and Invariant, Precondition, and Postcondition COT reasoners, we examined the safety of natural language task prompts, task plans, and task allocation outputs generated by LLM-based robotic systems as means of investigating and enhancing system safety profile. Our results show that SafePlan outperforms baseline models by leading to 90.5% reduction in harmful task prompt acceptance while still maintaining reasonable acceptance of safe tasks.