# Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees

arXiv: https://arxiv.org/abs/2501.16539 · HTML: https://mlsanity.com/paper/2501.16539/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://mlsanity.com/paper/2002.06305/) (2020)
- [Generalization in Cooperative Multi-Agent Systems](https://mlsanity.com/paper/2202.00104/) (2022)
- [Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs](https://mlsanity.com/paper/1703.04211/) (2017)
- [Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)](https://mlsanity.com/paper/2003.10662/) (2020)
- [Cooperative Multi-Agent Planning: A Survey](https://mlsanity.com/paper/1711.09057/) (2017)
- [Interaction-Aware Trajectory Planning for Autonomous Vehicles with Analytic Integration of Neural Networks into Model Predictive Control](https://mlsanity.com/paper/2301.05393/) (2023)
- [Multi-Robot Cooperative Navigation in Crowds: A Game-Theoretic Learning-Based Model Predictive Control Approach](https://mlsanity.com/paper/2310.06964/) (2023)
- [Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation](https://mlsanity.com/paper/2404.01746/) (2024)
- [Incentivizing Collaboration in Heterogeneous Teams via Common-Pool Resource Games](https://mlsanity.com/paper/1908.03938/) (2019)
- [Social Navigation in Crowded Environments with Model Predictive Control and Deep Learning-Based Human Trajectory Prediction](https://mlsanity.com/paper/2309.16838/) (2023)
- [Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams](https://mlsanity.com/paper/2109.10106/) (2021)

## Built on by (most cited first, 9 of 9)

- [Large Language Models for Multi-Robot Systems: A Survey](https://mlsanity.com/paper/2502.03814/) (2025)
- [GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks](https://mlsanity.com/paper/2503.06514/) (2025)
- [Graph-Grounded LLMs: Leveraging Graphical Function Calling to Minimize LLM Hallucinations](https://mlsanity.com/paper/2503.10941/) (2025)
- [From Virtual Agents to Robot Teams: A Multi-Robot Framework Evaluation in High-Stakes Healthcare Context](https://mlsanity.com/paper/2506.03546/) (2025)
- [Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence](https://mlsanity.com/paper/2510.26915/) (2025)
- [KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning](https://mlsanity.com/paper/2602.04129/) (2026)
- [Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams](https://mlsanity.com/paper/2603.08814/) (2026)
- [Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming](https://mlsanity.com/paper/2606.09919/) (2026)
- [Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shifts](https://mlsanity.com/paper/2608.21554/) (2026)

## Abstract (arXiv)

We present a novel mission-planning strategy for heterogeneous multi-robot teams, taking into account the specific constraints and capabilities of each robot. Our approach employs hierarchical trees to systematically break down complex missions into manageable sub-tasks. We develop specialized APIs and tools, which are utilized by Large Language Models (LLMs) to efficiently construct these hierarchical trees. Once the hierarchical tree is generated, it is further decomposed to create optimized schedules for each robot, ensuring adherence to their individual constraints and capabilities. We demonstrate the effectiveness of our framework through detailed examples covering a wide range of missions, showcasing its flexibility and scalability.