Create README.md

README.md

@@ -0,0 +1,73 @@
+---
+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}
+}
+```