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VLAN

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The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 84, in _split_generators
                  raise ValueError(
                  ...<2 lines>...
                  )
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Vision-Language Accessible Navigation (VLAN) Dataset

Overview

VLAN is a user-conditioned accessible navigation dataset for multimodal navigation research. This release contains the evaluation split used for benchmarking navigation outputs across heterogeneous users:

humanoid
blind / BVI
users with mobility impairments / wheelchair users
drone
quadrupedRobot

Each trajectory is paired with:

egocentric keyframes
a trajectory description
user-conditioned accessibility labels
user-specific target outputs

This dataset is designed to support two related tasks:

Action Prediction Navigation
- drone
- quadrupedRobot
Language-Guided Navigation
- humanoid
- blind / BVI
- users with mobility impairments / wheelchair users

Release Contents

This Hugging Face dataset repo is organized into two main folders:

vlan/: compressed full evaluation split archives
samples/: a small browsable subset of scenes and trajectories

`vlan/`

The vlan/ folder contains the full evaluation split as 10 compressed archives:

vlan_part01.tar.zst
vlan_part02.tar.zst
vlan_part03.tar.zst
vlan_part04.tar.zst
vlan_part05.tar.zst
vlan_part06.tar.zst
vlan_part07.tar.zst
vlan_part08.tar.zst
vlan_part09.tar.zst
vlan_part10.tar.zst

These 10 archives together contain:

Each archive can be downloaded and extracted independently.

Example extraction:

tar --zstd -xf vlan_part01.tar.zst

`samples/`

The samples/ folder contains a small subset of extracted scenes for quick inspection without downloading the full benchmark.

Scene and Trajectory Structure

After extraction, the dataset is organized as:

<scene_name>/
  <traj_name>/
    KeyFrames/
      00074.png
      00089.png
      ...
    metadata.json
    trajectory.json
    target.json

For example:

0001/
  01/
    KeyFrames/
      00074.png
      00089.png
      ...
    metadata.json
    trajectory.json
    target.json

File Descriptions

`KeyFrames/`

Contains egocentric RGB keyframes for the trajectory.

`metadata.json`

Contains:

scene_name
traj_name
scene taxonomy metadata:
- lighting
- scene_type
- path_geometry
- weather
embedded keyframe metadata

`trajectory.json`

Contains the trajectory-level motion and obstacle information, including:

obstacle geometry
poses and trajectory states
user-conditioned accessibility labels

The trajectory-level accessibility labels are stored under:

obstacles[0].userAccessibility

User keys:

humanoid
bvi
wheelchair
drone
quadrupedRobot

`target.json`

Contains the benchmark target definition for this trajectory, including:

corrected user-conditioned accessibility labels
user-specific ground-truth outputs
obstacle summary
goal information
action references for action-prediction users

User keys in target.json:

humanoid
blind
wheelchair
drone
quadrupedRobot

Accessibility Labels

The dataset uses four accessibility labels:

Passable
Bypass
HighRisk
Blocked

These labels are user-conditioned. The same scene and obstacle may have different labels for different users.

Examples:

a low obstacle may be Passable for a drone
the same obstacle may be Bypass for a humanoid user
the same obstacle may be HighRisk for a blind user
the same obstacle may be Blocked for a wheelchair user

Scene Taxonomy

Each scene is annotated with scene-level taxonomy metadata:

lighting
scene_type
path_geometry
weather

These fields are stored in each trajectory's metadata.json.

Intended Use

This dataset is intended for:

multimodal accessible navigation
user-conditioned navigation reasoning
action prediction for embodied navigation agents
language-guided instruction navigation
accessibility-aware navigation evaluation

Notes

The benchmark targets are stored in target.json.
The included samples/ folder is for quick inspection and is not a separate split.

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Total file size:

20.7 GB