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Datasets:
Louj
/
VoLN-UAV-dataset

Tasks:
Robotics
Reinforcement Learning
Languages:
English
ArXiv:
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label train
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label train

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VoLN-UAV Dataset

This release contains the navigation trajectories and benchmark metadata used by VoLN-UAV. Simulator environments are distributed separately.

Hugging Face Entries

Data Organization

The release contains 2,190 source-route candidates from four environments. The canonical benchmark keeps 1,786 episodes after start/goal candidate deduplication. Source candidates are retained so that the released observations and benchmark construction remain independently inspectable.

The package provides:

  • scene-level Train/Validation/Test split manifests;
  • route JSON files with RGB frame references and pose-derived state fields;
  • episode-level active beacons, benchmark records, templates, and checksums;
  • complete RGB observations under source/frames/ in the full release.

Usage

  1. Download the dataset package and the env package.
  2. Unzip the dataset package.
  3. Set source_root in the benchmark config to the unzipped source/ directory.
  4. Run python -m voln_uav.cli.build_benchmark --config <config.yaml> if the benchmark needs to be regenerated.

The generated manifest.json contains the release summary and Hugging Face resource links.

Citation

If you find this dataset useful, please consider citing the VoLN paper.

Download Layout

The dataset is uploaded as independent ZIP shards under metadata/, train/, val/, and test/. Each shard is below 5 GB. Extract metadata/VoLN-UAV-metadata.zip first, then extract the split shards you need into the same directory so that paths such as source/frames/<scene>/<trajectory>/<frame>.png match the JSONL metadata.

The train, validation, and test splits are episode-disjoint. The test split is held out on a separate scene, while train and validation may share scenes.

Use SHA256SUMS.txt to verify downloaded shards.
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Paper for Louj/VoLN-UAV-dataset