--- 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_ |--worker_graph.json |--rgb_.jpg |--depth_.jpg |--traj_Ackermanville |--worker_graph.json |--rgb_00001.jpg |--rgb_00002.jpg ... |--depth_00001.jpg |--depth_00002.jpg ... ... |--Matterport |--traj_ |--worker_graph.json |--rgb_.jpg |--depth_.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_ |--rgb_.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_ |--worker_graph.json |--rgb_.jpg |--depth_.jpg ``` where `````` 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": {"img_path": "./human_click_dataset/traj_/rgb_.jpg", "depth_path": "./human_click_dataset/traj_/depth_.png", "location": [, , ], "orientation": , "click_point": [, ], "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": [, , ], "landmarks": [[[, ], ], ...], "actions": ["ACTION_NAME", "turn_right", "move_forward", "turn_right", ...] "env_name": } ``` where ```[, , ]``` is the 3-axis location vector, `````` is the orientation only in simulation. ```[, ]``` 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 ```[, ]``` 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.