# EFLUX: Elastic Multi-Robot Formation Navigation and Adaptation with Agentic LLMs

arXiv: https://arxiv.org/abs/2607.12050 · HTML: https://mlsanity.com/paper/2607.12050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 12 of 12)

- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation](https://mlsanity.com/paper/2506.01538/) (2025)
- [Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2111.07334/) (2021)
- [Distributed Swarm Trajectory Optimization for Formation Flight in Dense Environments](https://mlsanity.com/paper/2109.07682/) (2021)
- [GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models](https://mlsanity.com/paper/2503.23875/) (2025)
- [Compositional Coordination for Multi-Robot Teams with Large Language Models](https://mlsanity.com/paper/2507.16068/) (2025)
- [Robust and Efficient Trajectory Planning for Formation Flight in Dense Environments](https://mlsanity.com/paper/2210.04048/) (2022)
- [Coordinated Multi-Robot Navigation with Formation Adaptation](https://mlsanity.com/paper/2404.01618/) (2024)
- [Subteaming and Adaptive Formation Control for Coordinated Multi-Robot Navigation](https://mlsanity.com/paper/2509.16412/) (2025)
- [Number Adaptive Formation Flight Planning via Affine Deformable Guidance in Narrow Environments](https://mlsanity.com/paper/2509.18636/) (2025)

## Abstract (arXiv)

Multi-robot teams operating in confined or cluttered environments must adapt both their formation geometry and group topology to navigate through complex obstacles. This adaptation requires two complementary behaviors: deformation, where the team continuously reshapes its geometry while remaining connected, and reconfiguration, where robots split into subgroups or merge back into a single formation. Existing methods often model these behaviors independently, connect them through handcrafted rules, or lack explicit geometric criteria for determining when each behavior should be invoked. However, challenging environments may require online changes in formation shape, connectivity, and effective team composition, making decoupled or rule-based approaches prone to suboptimal trajectories and deadlock. We propose EFLUX, a geometry-grounded LLM agentic framework for automatic and elastic multi-robot formation navigation. EFLUX extracts a structured scene representation and uses an LLM to reason jointly over both deformation actions, such as scaling and shearing, and reconfiguration actions, such as splitting and merging. These strategies are then translated into executable per-robot waypoints through a closed-loop generation, verification, and correction pipeline. Simulation and hardware experiments show that EFLUX enables safe, continuous, and elastic formation navigation in constrained environments, reducing deadlock and navigation failures compared with baselines while maintaining coherent multi-robot coordination.