| --- |
| 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} |
| } |
| ``` |
