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Datasets:
zzcnewly
/
RAVEN_QA

Tasks:
Robotics
Question Answering
Size:
n<1K
Tags:
Navigation
Retrieval
Visual Embedding
Multimodal
VLM
MLLM
License:
Dataset card Files
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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 RAVEN_QA@1699f9cc9bb1b3de39920dea894aff7e8e59863a
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 478, 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 RAVEN_QA@1699f9cc9bb1b3de39920dea894aff7e8e59863a

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RAVEN: Long-Horizon Reasoning and Navigation with a Visuo-Spatial-Temporal Memory

Website Code Citation

Introduction: We release the RAVEN-QA dataset.

  • Task: given a subsampled list of frames from a long video, each paired with timestamps and positions, and given a user query, the model or robot will find the time or positions where the queried thing shows up. The query can be objects, places, events, and concepts.

  • Categories: They cover dominant and secondary object retrieval (dominant or secondary in view), visual reasoning, commonsense reasoning, information recall, and spatial understanding.

  • Diversity: The dataset covers real-world and simulation robot view videos, web-sourced human view videos, and our self-recorded tour videos.

Parts

  1. irs: A simple retrieval dataset, including YouTube indoor and outdoor videos. (6 videos; 54 queries; with text and image queries)
  2. irs_hard: A harder human-ego retrieval dataset, including self-recorded and web-sourced videos for more challenging object-finding. (3 videos; 41 queries; with text queries)
  3. habitat_sim: A robot simulation dataset in Habitat environments. (19 videos; 157 queris; with text queries)
  4. real_world: A real-world robot exploration dataset taken in our labs and public areas. (4 videos; 21 queries; with text queries)

File Structure 

dataset_name/
├─ subsplit_name/
│  ├─ video_name/
│  │  ├─ frames/
│  │  │  ├─ frame_001.jpg
│  │  │  ├─ frame_002.jpg
│  │  │  └─ ...
│  │  └─ questions.json
│  ├─ ...
│  │
├─ subsplit_name/
│  ├─ video_name/

Citation

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