# Swarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design

arXiv: https://arxiv.org/abs/2312.01059 · HTML: https://mlsanity.com/paper/2312.01059/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2023.

## Built on by (most cited first, 15 of 15)

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- [Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact](https://mlsanity.com/paper/2507.00951/) (2025)
- [GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models](https://mlsanity.com/paper/2503.23875/) (2025)
- [Say the Mission, Execute the Swarm: Agent-Enhanced LLM Reasoning in the Web-of-Drones](https://mlsanity.com/paper/2605.03788/) (2026)
- [Agentic Aerial Cinematography: From Dialogue Cues to Cinematic Trajectories](https://mlsanity.com/paper/2509.16176/) (2025)
- [Agentic AI: The Era of Semantic Decoding](https://mlsanity.com/paper/2403.14562/) (2024)
- [Selective Agentic Recovery for UAV Autonomy with a Persistent Mission Runtime](https://mlsanity.com/paper/2606.14219/) (2026)
- [PrivLLMSwarm: Privacy-Preserving LLM-Driven UAV Swarms for Secure IoT Surveillance](https://mlsanity.com/paper/2512.06747/) (2025)
- [UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents](https://mlsanity.com/paper/2609.18326/) (2026)

## Abstract (arXiv)

This paper presents Swarm-GPT, a system that integrates large language models (LLMs) with safe swarm motion planning - offering an automated and novel approach to deployable drone swarm choreography. Swarm-GPT enables users to automatically generate synchronized drone performances through natural language instructions. With an emphasis on safety and creativity, Swarm-GPT addresses a critical gap in the field of drone choreography by integrating the creative power of generative models with the effectiveness and safety of model-based planning algorithms. This goal is achieved by prompting the LLM to generate a unique set of waypoints based on extracted audio data. A trajectory planner processes these waypoints to guarantee collision-free and feasible motion. Results can be viewed in simulation prior to execution and modified through dynamic re-prompting. Sim-to-real transfer experiments demonstrate Swarm-GPT's ability to accurately replicate simulated drone trajectories, with a mean sim-to-real root mean square error (RMSE) of 28.7 mm. To date, Swarm-GPT has been successfully showcased at three live events, exemplifying safe real-world deployment of pre-trained models.