Tr3itz/Geometry-Guided_Representation_for_Autonomous_Navigation

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 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:

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:

@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}
}