zju3dv / SuperNav

An Agentic Navigation System for Any Task in Any Scene 
 English | 简体中文 
 Jinkai Zhang 1 ·
 Jingyi Xu 1 ·
 Yuanhong Yu 1 ·
 Jiarui Guo 1 ·
 Ruizhen Hu 2 · 
 Hujun Bao 1 ·
 Xiaowei Zhou 1 ·
 Sida Peng 1,3† 
 1 Zhejiang University    2 Shenzhen University    3 Causa Robotics 
 † Corresponding author
 Overview Video 
 Watch on the project page ·
 Open MP4 
## 📢 News
> **[2026-10]** 🎉 The InteriorGS **ObjectNav and Multi-ObjectNav test episodes** (150 each, with reference trajectories) are released in HabitatGS-Benchmark. See the benchmark guide for data and evaluation.
Download the original scene data from InteriorGS.
The public `habitat_gs/objectnav/v1` files need conversion before `supernav run`.
See the SuperNav GS workflow for conversion,
running, primitive-trace export and SR/SPL plus SR1–SR5 scoring.
## 🧭 What Is SuperNav?
**SuperNav** is an agentic navigation system that uses a pretrained multimodal
model to solve diverse navigation tasks through tool use, task-progress tracking,
and context management, without navigation-specific fine-tuning of the model.
Tasks range from finding a single object and visiting multiple targets to
fulfilling high-level requests.
This repository provides the agent runtime, navigation tools and Skills,
experiment recipes, and evaluation utilities for **Habitat-GS** and **AI2-THOR**.
With SuperNav, you can:
- **Run navigation agents** with Codex, OpenCode, or Kimi.
- **Compose tasks, tools, and Skills** through explicit experiment configurations.
- **Watch execution live**, including RGB observations, trajectories, and tool calls.
- **Inspect and evaluate runs** using saved prompts, tool results, native logs,
 and independent task metrics.
See the project page for simulation
videos, the method overview, and research results.
Real-world robot demonstrations are on the project page; this repository currently
contains no real-robot drivers.
## 📦 Release Status
- [x] Agent runtime, navigation tools and Skills, experiment recipes, and evaluation
- [x] Learned Executor weights (Hugging Face)
- [x] InteriorGS single/multi-object navigation benchmark (HabitatGS-Benchmark)
- [ ] AI2-THOR demand-driven navigation benchmark
- [ ] Real-robot deployment (Unitree Go2): four-view RGB with LiDAR-based geometric execution
## 📖 Table of Contents
- Release Status
- Installation
- Quick Start
- Run Navigation Experiments
- Visualization and Evaluation
- Documentation and Development
- Citation
- Acknowledgements and License
## 🛠️ Installation
Use **Python 3.12**. Start with an independent environment for SuperNav:
 [code] 
The base installation supports configuration inspection, Habitat dry-runs, and
the browser demo. Install the extras needed for your workflow:
| Workflow | Install command |
| --- | --- |
| Agents and evaluation | `python -m pip install -e '.[agents,evaluation]'` |
| AI2-THOR experiments | `python -m pip install -e '.[ai2thor,agents,evaluation]'` |
| Rerun viewer | `python -m pip install -e '.[viewer]'` |
For Codex,