README.md

---
license: mit
language:
- en
tags:
- Visual Nagivation
- Proxy Map
- Waypoint
- Reinforcement Learning
- Contrastive Learning
- Intuitive Robot Motion Intent Visualization
---

# LAVN Dataset
Accepted to [HRI2025 Short Contributions](https://humanrobotinteraction.org/2025/short-contributions/)

Preprint: [arxiv.org/pdf/2308.16682](arxiv.org/pdf/2308.16682)

### Dataset Organization

After downloading and unzipping the zip files, please reorganize the files in the following tructure:
```
LAVN
   |--src
      |--makeData_virtual.py
      |--makeData_real.py
      ...
   |--Virtual
      |--Gibson
         |--traj_<SCENE_ID>
            |--worker_graph.json
            |--rgb_<FRAME_ID>.jpg
            |--depth_<FRAME_ID>.jpg
         |--traj_Ackermanville
            |--worker_graph.json
            |--rgb_00001.jpg
            |--rgb_00002.jpg
            ...
            |--depth_00001.jpg
            |--depth_00002.jpg
            ...
         ...
      |--Matterport
         |--traj_<SCENE_ID>
            |--worker_graph.json
            |--rgb_<FRAME_ID>.jpg
            |--depth_<FRAME_ID>.jpg
         |--traj_00000-kfPV7w3FaU5
            |--worker_graph.json
            |--rgb_00001.jpg
            |--rgb_00002.jpg
            ...
            |--depth_00001.jpg
            |--depth_00002.jpg
            ...
         ...
   |--Real
      |--Campus
         |--worker_graph.json
         |--traj_480p_<SCENE_ID>
            |--rgb_<FRAME_ID>.jpg
         |--traj_480p_scene00
            |--rgb_00001.jpg
```
where the main landmark annotation scripts ```makeData_virtual.py``` and ```makeData_real.py``` are in folder (1) ```src```. (2) ```Virtual``` and (3) ```Real``` store trajectories collected in the simulation and real world, respectively. Each trajectory's data is collected in the following format:
```
         |--traj_<SCENE_ID>
            |--worker_graph.json
            |--rgb_<FRAME_ID>.jpg
            |--depth_<FRAME_ID>.jpg
```
where ```<SCENE_ID>``` matches exactly the original one in [Gibson](https://github.com/StanfordVL/GibsonEnv/blob/master/gibson/data/README.md) and [Matterport](https://aihabitat.org/datasets/hm3d/) run by the photo-realistic simulator [Habitat](https://github.com/facebookresearch/habitat-sim). Images are saved in either ```.jpg``` or ```.png``` format. Note that ```rgb``` images are the main visual representation while ```depth``` is the auxiliary visual information captured only in the virtual environment. Real-world RGB images are downsampled to a ```640 × 480``` resolution noted by ```480p``` in a trajectory folder name.

```worker_graph.json``` stores the meta data in dictionary in Python saved in ```json``` file with the following format:

```
{"node<NODE_ID>":
  {"img_path": "./human_click_dataset/traj_<SCENE_ID>/rgb_<FRAME_ID>.jpg",
   "depth_path": "./human_click_dataset/traj_<SCENE_ID>/depth_<FRAME_ID>.png",
   "location": [<LOC_X>, <LOC_Y>, <LOC_Z>],
   "orientation": <ORIENT>,
   "click_point": [<COOR_X>, <COOR_Y>],
   "reason": ""},
  ...
 "node0":
  {"img_path": "./human_click_dataset/traj_00101-n8AnEznQQpv/rgb_00002.jpg",
   "depth_path": "./human_click_dataset/traj_00101-n8AnEznQQpv/depth_00002.jpg",
   "location": [0.7419548034667969, -2.079209327697754, -0.5635206699371338],
   "orientation": 0.2617993967423121,
   "click_point": [270, 214],
   "reason": ""}
 ...
 "edges":...
 "goal_location": null,
 "start_location": [<LOC_X>, <LOC_Y>, <LOC_Z>],
 "landmarks": [[[<COOR_X>, <COOR_Y>], <FRAME_ID>], ...],
 "actions": ["ACTION_NAME", "turn_right", "move_forward", "turn_right", ...]
 "env_name": <SCENE_ID>
}
```
where ```[<LOC_X>, <LOC_Y>, <LOC_Z>]``` is the 3-axis location vector, ```<ORIENT>``` is the orientation only in simulation. ```[<COOR_X>, <COOR_Y>]``` are the image coordinates of landmarks. ```ACTION_NAME``` stores the action of the robot take from the current frame to the next frame.


### Dataset Usage
The visual navigation task can be formulated as various types of problems, including but not limited to:

**1. Supervised Learning** by mapping visual observations (```RGBD```) to waypoints (image coordinates). A developer can
design a vision network whose input (```X```) is ```RGBD``` and output (```Y```) is image coordinate, specified by ```img_path```, ```depth_path```
and click point ```[<COOR_X>, <COOR_Y>]``` in the worker ```graph.json``` file in the dataset. The loss function can
be designed to minimize the discrepancy between the predicted image coordinate (```Y_pred```) and the ground truth (```Y```), e.g.
```loss = ||Y_pred − Y||```. Then ```Y_pred``` can be simply translated to a robot’s moving action, such as ```Y_pred``` in the center or
top region of an image means moving forward while ```left/right``` regions represent turning left or right.


**2. Map Representation Learning** in the latent space of a neural network. One can train this latent space to represent two
observations’ proximity by contrastive learning. The objective is to learn a function ```h()``` that predicts the distance given two
observations (```X1```) and (```X2```): ```dist = h(X1, X2)```. Note that ```dist()``` can be a cosine or distance-based function, depending on
the design choice. The positive samples can be nodes (a node includes information at a timestep such as ```RGBD``` data and image
coordinates) nearby while further nodes can be treated as negative samples. A landmark is a sparse and distinct object or scene
in the dataset that facilitates a more structured and global connection between nodes, which further assists in navigation in
more complex or longer trajectories.



### Long-Term Maintenance Plan
We will conduct a long-term maintenance plan to ensure the accessability and quality for future research:

**Data Standards**: Data formats will be checked regularly with scripts to validate data consistency.

**Data Cleaning**: Data in incorrect formats, missing data or contains invalid values will be removed.

**Scheduled Updates**: We set up montly schedule for data updates.

**Storage Solutions**: HuggingFace, with DOI (doi:10.57967/hf/2386), is provided as a public repository for online storage. A second copy will be stored in a private cloud server while a third copy will be stored in a local drive.

**Data Backup**: Once one of the copies in the aforementioned storage approach is detected inaccessible, it will be restored by one of the other two copies immediately.

**Documentation**: Our documentation will be updated regularly reflecting feedback from users.



### Citation
```
@article{johnson2024landmark,
  title={A Landmark-Aware Visual Navigation Dataset},
  author={Johnson, Faith and Cao, Bryan Bo and Dana, Kristin and Jain, Shubham and Ashok, Ashwin},
  journal={arXiv preprint arXiv:2402.14281},
  year={2024}
}
```
```
@misc{visnavdataset_lavn,
  author       = {visnavdataset},
  title        = {LAVN Dataset},
  year         = 2025,
  doi          = {10.57967/hf/2386},
  url          = {https://huggingface.co/datasets/visnavdataset/lavn},
  note         = {Accessed: 2025-02-07}
}
```
Note: change the accessed date.