# Open-Architecture End-to-End System for Real-World Autonomous Robot Navigation

arXiv: https://arxiv.org/abs/2410.06239 · HTML: https://mlsanity.com/paper/2410.06239/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2024.

## Built on by (most cited first, 5 of 5)

- [Grounded Vision-Language Navigation for UAVs with Open-Vocabulary Goal Understanding](https://mlsanity.com/paper/2506.10756/) (2025)
- [MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning](https://mlsanity.com/paper/2505.03035/) (2025)
- [SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation](https://mlsanity.com/paper/2505.13729/) (2025)
- [Object-Centric Representation Learning for Enhanced 3D Semantic Scene Graph Prediction](https://mlsanity.com/paper/2510.04714/) (2025)
- [Human Centric General Physical Intelligence for Agile Manufacturing Automation](https://mlsanity.com/paper/2508.11960/) (2025)

## Abstract (arXiv)

Enabling robots to autonomously navigate unknown, complex, and dynamic real-world environments presents several challenges, including imperfect perception, partial observability, localization uncertainty, and safety constraints. Current approaches are typically limited to simulations, where such challenges are not present. In this work, we present a lightweight, open-architecture, end-to-end system for real-world robot autonomous navigation. Specifically, we deploy a real-time navigation system on a quadruped robot by integrating multiple onboard components that communicate via ROS2. Given navigation tasks specified in natural language, the system fuses onboard sensory data for localization and mapping with open-vocabulary semantics to build hierarchical scene graphs from a continuously updated semantic object map. An LLM-based planner leverages these graphs to generate and adapt multi-step plans in real time as the scene evolves. Through experiments across multiple indoor environments using a Unitree Go2 quadruped, we demonstrate zero-shot real-world autonomous navigation, achieving over 88% task success, and provide analysis of system behavior during deployment.