# GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models

arXiv: https://arxiv.org/abs/2503.23875 · HTML: https://mlsanity.com/paper/2503.23875/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 49 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023)
- [Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf](https://mlsanity.com/paper/2309.04658/) (2023)
- [RoCo: Dialectic Multi-Robot Collaboration with Large Language Models](https://mlsanity.com/paper/2307.04738/) (2023)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach](https://mlsanity.com/paper/2312.11865/) (2023)
- [Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds](https://mlsanity.com/paper/2310.13255/) (2023)
- [Creative Robot Tool Use with Large Language Models](https://mlsanity.com/paper/2310.13065/) (2023)
- [RobotGPT: Robot Manipulation Learning from ChatGPT](https://mlsanity.com/paper/2312.01421/) (2023)
- [Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet](https://mlsanity.com/paper/2401.03630/) (2024)
- [Embodied Evolution in Collective Robotics: A Review](https://mlsanity.com/paper/1709.08992/) (2017)
- [Swarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design](https://mlsanity.com/paper/2312.01059/) (2023)
- [FlockGPT: Guiding UAV Flocking with Linguistic Orchestration](https://mlsanity.com/paper/2405.05872/) (2024)
- [LLM2Swarm: Robot Swarms that Responsively Reason, Plan, and Collaborate through LLMs](https://mlsanity.com/paper/2410.11387/) (2024)
- [ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models](https://mlsanity.com/paper/2404.02318/) (2024)
- [Challenges Faced by Large Language Models in Solving Multi-Agent Flocking](https://mlsanity.com/paper/2404.04752/) (2024)
- [Bearing Rigidity Theory and its Applications for Control and Estimation of Network Systems: Life Beyond Distance Rigidity](https://mlsanity.com/paper/1803.05505/) (2018)

## Built on by (most cited first, 4 of 4)

- [Compositional Coordination for Multi-Robot Teams with Large Language Models](https://mlsanity.com/paper/2507.16068/) (2025)
- [EFLUX: Elastic Multi-Robot Formation Navigation and Adaptation with Agentic LLMs](https://mlsanity.com/paper/2607.12050/) (2026)
- [A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local Learning](https://mlsanity.com/paper/2609.05133/) (2026)
- [Waggle: Learning One Anonymous Local Law for Self-Organizing LLM Swarms](https://mlsanity.com/paper/2609.34136/) (2026)

## Abstract (arXiv)

The development of control policies for multi-robot systems traditionally follows a complex and labor-intensive process, often lacking the flexibility to adapt to dynamic tasks. This has motivated research on methods to automatically create control policies. However, these methods require iterative processes of manually crafting and refining objective functions, thereby prolonging the development cycle. This work introduces textitGenSwarm, an end-to-end system that leverages large language models to automatically generate and deploy control policies for multi-robot tasks based on simple user instructions in natural language. As a multi-language-agent system, GenSwarm achieves zero-shot learning, enabling rapid adaptation to altered or unseen tasks. The white-box nature of the code policies ensures strong reproducibility and interpretability. With its scalable software and hardware architectures, GenSwarm supports efficient policy deployment on both simulated and real-world multi-robot systems, realizing an instruction-to-execution end-to-end functionality that could prove valuable for robotics specialists and non-specialists alike.The code of the proposed GenSwarm system is available online: https://github.com/WindyLab/GenSwarm.