Quadrupped-Legged Robot Movement Plan Generation using Large Language Model
Abstract
Traditional control interfaces for quadruped robots often impose a high barrier to entry, requiring specialized technical knowledge for effective operation. To address this, this paper presents a novel control framework that integrates Large Language Models (LLMs) to enable intuitive, natural language-based navigation. We propose a distributed architecture where high-level instruction processing is offloaded to an external server to overcome the onboard computational constraints of the DeepRobotics Jueying Lite 3 platform. The system grounds LLM-generated plans into executable ROS navigation commands using real-time sensor fusion (LiDAR, IMU, and Odometry). Experimental validation was conducted in a structured indoor environment across four distinct scenarios, ranging from single-room tasks to complex cross-zone navigation. The results demonstrate the system's robustness, achieving an aggregate success rate of over 90\% across all scenarios, validating the feasibility of offloaded LLM-based planning for autonomous quadruped deployment in real-world settings.
Cite
@article{arxiv.2512.21293,
title = {Quadrupped-Legged Robot Movement Plan Generation using Large Language Model},
author = {Muhtadin and Vincentius Gusti Putu A. B. M. and Ahmad Zaini and Mauridhi Hery Purnomo and I Ketut Eddy Purnama and Chastine Fatichah},
journal= {arXiv preprint arXiv:2512.21293},
year = {2026}
}