# AuDeRe: Automated Strategy Decision and Realization in Robot Planning and Control via LLMs

arXiv: https://arxiv.org/abs/2504.03015 · HTML: https://mlsanity.com/paper/2504.03015/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2025.

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

- [Collision- and Reachability-Aware Multi-Robot Control with Grounded LLM Planners](https://mlsanity.com/paper/2505.20573/) (2025)
- [Compositional Coordination for Multi-Robot Teams with Large Language Models](https://mlsanity.com/paper/2507.16068/) (2025)
- [TACOS: Task Agnostic COordinator of a multi-drone System](https://mlsanity.com/paper/2510.01869/) (2025)
- [Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems](https://mlsanity.com/paper/2604.21138/) (2026)

## Abstract (arXiv)

Recent advancements in large language models (LLMs) have shown significant promise in various domains, especially robotics. However, most prior LLM-based work in robotic applications either directly predicts waypoints or applies LLMs within fixed tool integration frameworks, offering limited flexibility in exploring and configuring solutions best suited to different tasks. In this work, we propose a framework that leverages LLMs to select appropriate planning and control strategies based on task descriptions, environmental constraints, and system dynamics. These strategies are then executed by calling the available comprehensive planning and control APIs. Our approach employs iterative LLM-based reasoning with performance feedback to refine the algorithm selection. We validate our approach through extensive experiments across tasks of varying complexity, from simple tracking to complex planning scenarios involving spatiotemporal constraints. The results demonstrate that using LLMs to determine planning and control strategies from natural language descriptions significantly enhances robotic autonomy while reducing the need for extensive manual tuning and expert knowledge. Furthermore, our framework maintains generalizability across different tasks and notably outperforms baseline methods that rely on LLMs for direct trajectory, control sequence, or code generation.