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--- |
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license: cc-by-sa-4.0 |
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language: |
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- en |
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pretty_name: StreamVLN |
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extra_gated_prompt: >- |
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### StreamVLN COMMUNITY LICENSE AGREEMENT StreamVLN Release Date: Augest 20, |
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2025 All the data and code within this repo are under [CC BY-NC-SA |
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4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). |
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By clicking Submit, you accept the license terms and acknowledge that the |
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information you provide will be collected, stored, and processed for academic |
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tags: |
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- navigation |
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size_categories: |
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- n>1T |
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--- |
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This repo contains the data for the paper **"StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling."** |
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[](http://arxiv.org/abs/2507.05240) |
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[](https://streamvln.github.io/) |
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[](https://huggingface.co/papers/2507.05240/) |
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[](https://www.youtube.com/watch?v=gG3mpefOBjc) |
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## News |
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[2025/09/30] For R2R, we have now removed all v1 version data and only retained the v1-3 data. |
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[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. |
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## Overview |
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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. |
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## Data Collection |
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Data collected in this repo are from the following open-source datasets: |
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- [R2R-VLNCE](https://drive.google.com/file/d/18DCrNcpxESnps1IbXVjXSbGLDzcSOqzD/view) |
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- [RxR-VLNCE](https://drive.google.com/file/d/145xzLjxBaNTbVgBfQ8e9EsBAV8W-SM0t/view) |
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- [R2R-EnvDrop](https://drive.google.com/file/d/1fo8F4NKgZDH-bPSdVU3cONAkt5EW-tyr/view) |
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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. |
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## Dataset Description |
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### Dataset Structure |
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After **extracting `images.tar.gz`**, the dataset has the following structure: |
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```shell |
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StreamVLN-Trajectory-Data/ |
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├── R2R/ |
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│ ├── images/ |
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│ │ ├── 1LXtFkjw3qL_r2r_000087/ |
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│ │ │ └── rgb/ |
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│ │ │ ├── 000.jpg |
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│ │ │ ├── 001.jpg |
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│ │ │ └── ... |
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│ │ ├── 1LXtFkjw3qL_r2r_000099/ |
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│ │ ├── 1LXtFkjw3qL_r2r_000129/ |
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│ │ └── ... |
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│ └── annotations.json |
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├── RxR/ |
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│ ├── images/ |
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│ └── annotations.json |
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├── EnvDrop/ |
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│ └── annotations.json |
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└── ScaleVLN/ |
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├── annotations.json |
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└── scalevln_subset_150k.json.gz |
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``` |
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### Contents |
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`images/`: The folder contains the rgb observations collected from Habitat simulator. |
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`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: |
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```python |
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{ |
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"id": (int) Identifier for the episode, |
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"video": (str) Video ID to identify the relative path to the directory which contains the episode, format: "images/{scene}_{dataset_source}_{id}", |
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"instruction": (list[str]) Navigation instructions, |
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"actions": (list[int]) Discrete actions sequence in Habitat simulator, |
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# 1 = MoveForward (25cm) |
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# 2 = TurnLeft (15°) |
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# 3 = TurnRight (15°) |
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# -1 = Dummy |
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# 0 = Stop (omitted in annotations) |
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} |
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``` |
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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. |
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## EnvDrop & ScaleVLN Dataset Note |
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For **EnvDrop** and **ScaleVLN**, only **navigation annotations** are provided due to the large number of episodes. |
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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. |
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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:** |
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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**: |
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```yaml |
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habitat: |
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... |
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dataset: |
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... |
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data_path: data/datasets/envdrop/envdrop.json.gz |
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``` |
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2. Run the code below to save RGB images. |
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```python |
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import os |
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import json |
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import habitat |
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from habitat_baselines.config.default import get_config |
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from habitat.tasks.nav.shortest_path_follower import ShortestPathFollower |
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from streamvln.habitat_extensions import measures |
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CONFIG_PATH = "config/vln_r2r.yaml" # Path to the Habitat config file |
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ANNOT_PATH = "data/trajectory_data/EnvDrop/annotations.json" # Path to the annotations file |
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GOAL_RADIUS = 0.25 # Radius for the goal in meters. not used if get actions from annotations |
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env = habitat.Env(config=get_config(CONFIG_PATH)) |
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annotations = json.load(open(ANNOT_PATH, "r")) |
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for episode in env.episodes: |
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env.current_episode = episode |
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agent = ShortestPathFollower(sim=env.sim, goal_radius=GOAL_RADIUS, return_one_hot=False) |
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observation = env.reset() |
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annotation = next(annot for annot in annotations if annot["id"] == int(episode.episode_id)) # Get annotation for current episode |
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reference_actions = annotation["actions"][1:] + [0] # Pop the dummy action at the beginning and add stop action at the end |
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step_id = 0 # Initialize step ID |
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while not env.episode_over: |
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rgb = observation["rgb"] # Get the current rgb observation |
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# TODO: Save RGB frame (customize as needed) |
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# -------------------------------------------------------- |
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import PIL.Image as Image |
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video_id = annotation["video"] # Get the video ID from the annotation |
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rgb_dir = f"data/trajectory_data/EnvDrop/{video_id}/rgb" |
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os.makedirs(rgb_dir, exist_ok=True) |
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Image.fromarray(rgb).convert("RGB").save(os.path.join(rgb_dir, f"{step_id:03d}.jpg")) |
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# -------------------------------------------------------- |
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action = reference_actions.pop(0) # Get next action from our annotation |
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observation = env.step(action) # Update observation |
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step_id += 1 |
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env.close() |
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``` |
