Datasets:
Modalities:
Image
Languages:
English
ArXiv:
Tags:
Visual Nagivation
Proxy Map
Waypoint
Reinforcement Learning
Contrastive Learning
Intuitive Robot Motion Intent Visualization
DOI:
License:
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README.md
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# LAVN Dataset
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### Data Organization
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After downloading and unzipping the zip files, please reorganize the files in the following tructure:
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```
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LAVN
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|--src
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|--makeData_virtual.py
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|--makeData_real.py
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...
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|--Virtual
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|--Gibson
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|--traj_<SCENE_ID>
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|--worker_graph.json
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|--rgb_<FRAME_ID>.jpg
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|--depth_<FRAME_ID>.jpg
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|--traj_Ackermanville
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|--worker_graph.json
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|--rgb_00001.jpg
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|--rgb_00002.jpg
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...
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|--depth_00001.jpg
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|--depth_00002.jpg
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...
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...
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|--Matterport
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|--traj_<SCENE_ID>
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|--worker_graph.json
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|--rgb_<FRAME_ID>.jpg
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|--depth_<FRAME_ID>.jpg
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|--traj_00000-kfPV7w3FaU5
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|--worker_graph.json
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|--rgb_00001.jpg
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|--rgb_00002.jpg
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...
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|--depth_00001.jpg
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|--depth_00002.jpg
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...
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...
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|--Real
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|--Campus
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|--worker_graph.json
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|--traj_480p_<SCENE_ID>
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|--rgb_<FRAME_ID>.jpg
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|--traj_480p_scene00
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|--rgb_00001.jpg
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```
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where the main landmark annotation scripts ```makeData_virtual.py``` and ```makeData_real.py``` are in folder (1) ```src```. (2) ```Virtual``` and (3) ```Real``` stores trajectories collecetd in the simulation and real world, respectively. In each trajectory's data is collected in the following format:
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```
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|--traj_<SCENE_ID>
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|--worker_graph.json
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|--rgb_<FRAME_ID>.jpg
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|--depth_<FRAME_ID>.jpg
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```
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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.
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```worker_graph.json``` stores the meta data in dictionary in Python saved in ```json``` file with the following format:
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```
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{"node<NODE_ID>":
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{"img_path": "./human_click_dataset/traj_<SCENE_ID>/rgb_<FRAME_ID>.jpg",
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"depth_path": "./human_click_dataset/traj_<SCENE_ID>/depth_<FRAME_ID>.png",
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"location": [<LOC_X>, <LOC_Y>, <LOC_Z>],
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"orientation": <ORIENT>,
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"click_point": [<COOR_X>, <COOR_Y>],
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"reason": ""},
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...
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"node0":
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{"img_path": "./human_click_dataset/traj_00101-n8AnEznQQpv/rgb_00002.jpg",
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"depth_path": "./human_click_dataset/traj_00101-n8AnEznQQpv/depth_00002.jpg",
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"location": [0.7419548034667969, -2.079209327697754, -0.5635206699371338],
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"orientation": 0.2617993967423121,
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"click_point": [270, 214],
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"reason": ""}
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...
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"edges":...
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"goal_location": null,
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"start_location": [<LOC_X>, <LOC_Y>, <LOC_Z>],
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"landmarks": [[[<COOR_X>, <COOR_Y>], <FRAME_ID>], ...],
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"actions": ["ACTION_NAME", "turn_right", "move_forward", "turn_right", ...]
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"env_name": <SCENE_ID>
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}
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```
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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.
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### Long-Term Maintenance Plan
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We will conduct a long-term maintenance plan to ensure the accessability and quality for future research:
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**Data Standards**: Data formats will be checked regularly with scripts to validate data consistency.
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**Data Cleaning**: Data in incorrect formats, missing data or contains invalid values will be removed.
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**Scheduled Updates**: We set up montly schedule for data updates.
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**Storage Solutions**: Zenodo with a DOI will be provided to as an 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.
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**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.
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**Documentation**: Our documentation will be updated regularly reflecting feedback from users.
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src.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:9d6014bd5ff85b89d4b3d40aa0ce4b5c00c8ee500f1e7595603021a96ee56e86
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size 7111
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