# LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics

arXiv: https://arxiv.org/abs/2312.01797 · HTML: https://mlsanity.com/paper/2312.01797/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [Path Planning using Neural A* Search](https://mlsanity.com/paper/2009.07476/) (2020)
- [Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning](https://mlsanity.com/paper/2310.03249/) (2023)
- [Language-Conditioned Path Planning](https://mlsanity.com/paper/2308.16893/) (2023)
- [A Human-friendly Verbal Communication Platform for Multi-Robot Systems: Design and Principles](https://mlsanity.com/paper/2211.09519/) (2022)
- [Fast-Spanning Ant Colony Optimisation (FaSACO) for Mobile Robot Coverage Path Planning](https://mlsanity.com/paper/2205.15691/) (2022)
- [Hybrid of representation learning and reinforcement learning for dynamic and complex robotic motion planning](https://mlsanity.com/paper/2309.03758/) (2023)

## Built on by (most cited first, 26 of 26)

- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model](https://mlsanity.com/paper/2401.02051/) (2024)
- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey](https://mlsanity.com/paper/2503.22458/) (2025)
- [AGILE: A Novel Reinforcement Learning Framework of LLM Agents](https://mlsanity.com/paper/2405.14751/) (2024)
- [Agent-Oriented Planning in Multi-Agent Systems](https://mlsanity.com/paper/2410.02189/) (2024)
- [LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://mlsanity.com/paper/2505.00753/) (2025)
- [Planning with Multi-Constraints via Collaborative Language Agents](https://mlsanity.com/paper/2405.16510/) (2024)
- [Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance](https://mlsanity.com/paper/2507.17131/) (2025)
- [LLM-based Automated Grading with Human-in-the-Loop](https://mlsanity.com/paper/2504.05239/) (2025)
- [A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models](https://mlsanity.com/paper/2405.18208/) (2024)
- [From Interaction to Impact: Towards Safer AI Agents Through Understanding and Evaluating Mobile UI Operation Impacts](https://mlsanity.com/paper/2410.09006/) (2024)
- [Exploring the Necessity of Reasoning in LLM-based Agent Scenarios](https://mlsanity.com/paper/2503.11074/) (2025)
- [A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy](https://mlsanity.com/paper/2506.09420/) (2025)
- [Look Further Ahead: Testing the Limits of GPT-4 in Path Planning](https://mlsanity.com/paper/2406.12000/) (2024)
- [Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2408.12548/) (2024)
- [LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences](https://mlsanity.com/paper/2509.12273/) (2025)
- [How Far are LLMs from Real Search? A Comprehensive Study on Efficiency, Completeness, and Inherent Capabilities](https://mlsanity.com/paper/2502.18387/) (2025)
- [MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation](https://mlsanity.com/paper/2602.05048/) (2026)
- [Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents](https://mlsanity.com/paper/2510.06078/) (2025)
- [Strategy-Augmented Planning for Large Language Models via Opponent Exploitation](https://mlsanity.com/paper/2505.08459/) (2025)
- [Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research](https://mlsanity.com/paper/2506.12312/) (2025)
- [DINOv3-Diffusion Policy: Self-Supervised Large Visual Model for Visuomotor Diffusion Policy Learning](https://mlsanity.com/paper/2509.17684/) (2025)
- [Overcoming the Weakest-Link Effect in LLM-Driven Program Optimization via Heterogeneous Edit Recombination](https://mlsanity.com/paper/2607.28947/) (2026)
- [Agentic AI: The Era of Semantic Decoding](https://mlsanity.com/paper/2403.14562/) (2024)
- [Deployability-Centric Infrastructure-as-Code Generation: Fail, Learn, Refine, and Succeed through LLM-Empowered DevOps Simulation](https://mlsanity.com/paper/2506.05623/) (2025)

## Abstract (arXiv)

This research focuses on how Large Language Models (LLMs) can help with (path) planning for mobile embodied agents such as robots, in a human-in-the-loop and interactive manner. A novel framework named LLM A*, aims to leverage the commonsense of LLMs, and the utility-optimal A* is proposed to facilitate few-shot near-optimal path planning. Prompts are used for two main purposes: 1) to provide LLMs with essential information like environments, costs, heuristics, etc.; 2) to communicate human feedback on intermediate planning results to LLMs. This approach takes human feedback on board and renders the entire planning process transparent (akin to a `white box') to humans. Moreover, it facilitates code-free path planning, thereby fostering the accessibility and inclusiveness of artificial intelligence techniques to communities less proficient in coding. Comparative analysis against A* and RL demonstrates that LLM A* exhibits greater efficiency in terms of search space and achieves paths comparable to A* while outperforming RL. The interactive nature of LLM A* also makes it a promising tool for deployment in collaborative human-robot tasks. Codes and Supplemental Materials can be found at GitHub: https://github.com/speedhawk/LLM-A-.