| --- |
| license: cc-by-sa-4.0 |
| language: |
| - en |
| pretty_name: StreamVLN |
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| ### StreamVLN COMMUNITY LICENSE AGREEMENT StreamVLN Release Date: Augest 20, |
| 2025 All the data and code within this repo are under [CC BY-NC-SA |
| 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). |
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| tags: |
| - navigation |
| size_categories: |
| - n>1T |
| --- |
| This repo contains the data for the paper **"StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling."** |
|
|
| [](http://arxiv.org/abs/2507.05240) |
| [](https://streamvln.github.io/) |
| [](https://huggingface.co/papers/2507.05240/) |
| [](https://www.youtube.com/watch?v=gG3mpefOBjc) |
|
|
|
|
| ## News |
| [2025/09/30] For R2R, we have now removed all v1 version data and only retained the v1-3 data. |
|
|
| [2025/08/20] We have updated the R2R to a new version, which now includes both the v1 and v1-3 datasets. Additionally. And we have fixed the episode ID issue in RxR to ensure compatibility with the currently available RxR download links. |
|
|
| ## Overview |
|
|
| The dataset consists of visual observations and annotations collected in the Matterport3D (MP3D) environment using the Habitat simulator. It combines data from several open-source Vision-and-Language Navigation (VLN) datasets. |
|
|
| ## Data Collection |
|
|
| Data collected in this repo are from the following open-source datasets: |
| - [R2R-VLNCE](https://drive.google.com/file/d/18DCrNcpxESnps1IbXVjXSbGLDzcSOqzD/view) |
| - [RxR-VLNCE](https://drive.google.com/file/d/145xzLjxBaNTbVgBfQ8e9EsBAV8W-SM0t/view) |
| - [R2R-EnvDrop](https://drive.google.com/file/d/1fo8F4NKgZDH-bPSdVU3cONAkt5EW-tyr/view) |
|
|
| To get actions and observations, we enable a `ShortestPathFollower` agent in the Habitat simulator to follow the subgoals and collect rgb observations along the path. The data is collected across the Matterport3D (MP3D) scenes. |
|
|
| ## Dataset Description |
|
|
| ### Dataset Structure |
|
|
| After **extracting `images.tar.gz`**, the dataset has the following structure: |
|
|
| ```shell |
| StreamVLN-Trajectory-Data/ |
| ├── R2R/ |
| │ ├── images/ |
| │ │ ├── 1LXtFkjw3qL_r2r_000087/ |
| │ │ │ └── rgb/ |
| │ │ │ ├── 000.jpg |
| │ │ │ ├── 001.jpg |
| │ │ │ └── ... |
| │ │ ├── 1LXtFkjw3qL_r2r_000099/ |
| │ │ ├── 1LXtFkjw3qL_r2r_000129/ |
| │ │ └── ... |
| │ └── annotations.json |
| ├── RxR/ |
| │ ├── images/ |
| │ └── annotations.json |
| ├── EnvDrop/ |
| │ └── annotations.json |
| └── ScaleVLN/ |
| ├── annotations.json |
| └── scalevln_subset_150k.json.gz |
| |
| ``` |
|
|
| ### Contents |
|
|
| `images/`: The folder contains the rgb observations collected from Habitat simulator. |
|
|
| `annotations.json`: The file contain the navigation instructions and discrete actions sequence from Habitat Simulator for each dataset. The structure of annotation for each episode is as follows: |
|
|
| ```python |
| { |
| "id": (int) Identifier for the episode, |
| "video": (str) Video ID to identify the relative path to the directory which contains the episode, format: "images/{scene}_{dataset_source}_{id}", |
| "instruction": (list[str]) Navigation instructions, |
| "actions": (list[int]) Discrete actions sequence in Habitat simulator, |
| # 1 = MoveForward (25cm) |
| # 2 = TurnLeft (15°) |
| # 3 = TurnRight (15°) |
| # -1 = Dummy |
| # 0 = Stop (omitted in annotations) |
| } |
| ``` |
|
|
| Each episode in the `annotations.json` file corresponds to a folder in the `images/` directory, where the folder name is included in the `video` ID. The rgb images are stored in the `rgb/` subdirectory of each episode folder. Length of the `actions` list corresponds to the number of rgb images in the episode to ensure observation-action data pairs. |
|
|
|
|
| ## EnvDrop & ScaleVLN Dataset Note |
| For **EnvDrop** and **ScaleVLN**, only **navigation annotations** are provided due to the large number of episodes. |
|
|
| Considering the discrete setting of the original **ScaleVLN** dataset, we provide the converted episodes in continuous environment settings in `StreamVLN-Trajectory-Data/ScaleVLN/scalevln_subset_150k.json.gz`. These episodes correspond to the annotations we have provided. The format of these episodes is consistent with R2R-CE. You can load these episodes in the same way as R2R/EnvDrop. |
|
|
| To obtain RGB observations, you can replay the annotated actions using the Habitat simulator. **Below is a example demonstrating how to replay stored actions and get EnvDrop RGB frames:** |
|
|
| 1. Before proceeding, you need to modify the configuration file to specify the path to the **EnvDrop** episodes. Please overwrite the `habitat.dataset.data_path` in **config/vln_r2r.yaml**: |
| |
| ```yaml |
| habitat: |
| ... |
| dataset: |
| ... |
| data_path: data/datasets/envdrop/envdrop.json.gz |
| ``` |
| |
| |
| 2. Run the code below to save RGB images. |
| |
| ```python |
| import os |
| import json |
| import habitat |
| |
| from habitat_baselines.config.default import get_config |
| from habitat.tasks.nav.shortest_path_follower import ShortestPathFollower |
| from streamvln.habitat_extensions import measures |
| |
| CONFIG_PATH = "config/vln_r2r.yaml" # Path to the Habitat config file |
| ANNOT_PATH = "data/trajectory_data/EnvDrop/annotations.json" # Path to the annotations file |
| GOAL_RADIUS = 0.25 # Radius for the goal in meters. not used if get actions from annotations |
| |
| env = habitat.Env(config=get_config(CONFIG_PATH)) |
| annotations = json.load(open(ANNOT_PATH, "r")) |
| |
| for episode in env.episodes: |
| env.current_episode = episode |
| agent = ShortestPathFollower(sim=env.sim, goal_radius=GOAL_RADIUS, return_one_hot=False) |
| observation = env.reset() |
| |
| annotation = next(annot for annot in annotations if annot["id"] == int(episode.episode_id)) # Get annotation for current episode |
| reference_actions = annotation["actions"][1:] + [0] # Pop the dummy action at the beginning and add stop action at the end |
| step_id = 0 # Initialize step ID |
| |
| while not env.episode_over: |
| rgb = observation["rgb"] # Get the current rgb observation |
| |
| # TODO: Save RGB frame (customize as needed) |
| # -------------------------------------------------------- |
| import PIL.Image as Image |
| video_id = annotation["video"] # Get the video ID from the annotation |
| rgb_dir = f"data/trajectory_data/EnvDrop/{video_id}/rgb" |
| os.makedirs(rgb_dir, exist_ok=True) |
| Image.fromarray(rgb).convert("RGB").save(os.path.join(rgb_dir, f"{step_id:03d}.jpg")) |
| # -------------------------------------------------------- |
| |
| action = reference_actions.pop(0) # Get next action from our annotation |
| observation = env.step(action) # Update observation |
| step_id += 1 |
| |
| env.close() |
| |
| ``` |
