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task_categories:
- robotics
- reinforcement-learning
language:
- en
tags:
- autonomous-navigation
- robot-navigation
- visual-navigation
- sim-to-real
- igibson
- turtlebot3
- rgb
- lidar
---
# Geometry-guided Representation for Autonomous Navigation
## Overview
The **Geometry-guided Representation for Autonomous Navigation** (_GRAN_) dataset is a collection of simulated robot trajectories designed to study **scene transfer** in autonomous navigation from visual observations.
The dataset contains trajectories collected by a TurtleBot3 robot in the [iGibson](https://github.com/StanfordVL/iGibson) simulation environment.
These trajectories are collected across multiple object configurations and visually different environments (background and floor), enabling the study of robust representation learning for vision-based navigation policies to generalize across changes in the appearence of the environment.
## Dataset Composition & Structure
The dataset is composed of:
- **2 rooms** simulated in the iGibson environment;
- **10** different **object settings** per room;
- **4 agents**, with a full knowledge of the environment, differing by the level of expereince;
- **5 trajectories** collected by each agent.
Each trajectory is a collection of RGB images captured by an onboard camera of the TB3 robot, and instantiated in **9 visually different environments**.
The structure of the dataset:
```bash
GRAN/
└── Room1/ # Room
└── Setting1/ # Room setting
├── 8m/ #
├── 6000000/ # Agents used for the collection of rollout trajectories.
├── 3200000/ # The level of experience is identified by the number of training steps.
└── 400000/ #
└── episode_0001 # Trajectory
├── episode_0001.pkl # Pandas DataFrame object containing per step additional information (e.g., robot's and target's absolute coordinates, LiDAR readings, etc.)
└── augmented_results # Trajectory of images collected in the 9 visually different environments
```
## Intended Use
The dataset is intended for research on:
- Robust Representation Learning
- Representation Learning guided by Privileged Information
- Navigation Policy Learning from visual observations
- Scene Transfer of Navigation Policies
## Citation
If you use this dataset in your research, please cite the associated work:
```bibtex
@article{zhalehmehrabi2026robust,
title={Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning},
author={Zhalehmehrabi, Amirhossein and Tezze, Tiziano and Castelini, Alberto and Farinelli, Alessandro},
journal={arXiv preprint arXiv:2606.05506},
year={2026}
}
``` |