Datasets:

smilegeng
/

stanford_hydra_dataset

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
action: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
  child 0, min: list<item: double>
      child 0, item: double
  child 1, max: list<item: double>
      child 0, item: double
  child 2, mean: list<item: double>
      child 0, item: double
  child 3, std: list<item: double>
      child 0, item: double
  child 4, count: list<item: int64>
      child 0, item: int64
timestamp: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
  child 0, min: list<item: double>
      child 0, item: double
  child 1, max: list<item: double>
      child 0, item: double
  child 2, mean: list<item: double>
      child 0, item: double
  child 3, std: list<item: double>
      child 0, item: double
  child 4, count: list<item: int64>
      child 0, item: int64
observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
  child 0, min: list<item: double>
      child 0, item: double
  child 1, max: list<item: double>
      child 0, item: double
  child 2, mean: list<item: double>
      child 0, item: double
  child 3, std: list<item: double>
      child 0, item: double
  child 4, count: list<item: int64>
      child 0, item: int64
frame_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
  chi
...
ld 5, episode_index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, names: null
      child 3, fps: double
  child 6, frame_index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, names: null
      child 3, fps: double
  child 7, next.reward: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, names: null
      child 3, fps: double
  child 8, next.done: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, names: null
      child 3, fps: double
  child 9, index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, names: null
      child 3, fps: double
  child 10, task_index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, names: null
      child 3, fps: double
to
{'codebase_version': Value('string'), 'robot_type': Value('string'), 'total_episodes': Value('int64'), 'total_frames': Value('int64'), 'total_tasks': Value('int64'), 'chunks_size': Value('int64'), 'fps': Value('int64'), 'splits': {'train': Value('string')}, 'data_path': Value('string'), 'video_path': Value('string'), 'features': {'observation.images.image': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': List(Value('string')), 'video_info': {'video.fps': Value('float64'), 'video.codec': Value('string'), 'video.pix_fmt': Value('string'), 'video.is_depth_map': Value('bool'), 'has_audio': Value('bool')}}, 'observation.images.wrist_image': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': List(Value('string')), 'video_info': {'video.fps': Value('float64'), 'video.codec': Value('string'), 'video.pix_fmt': Value('string'), 'video.is_depth_map': Value('bool'), 'has_audio': Value('bool')}}, 'observation.state': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': {'motors': List(Value('string'))}, 'fps': Value('float64')}, 'action': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': {'motors': List(Value('string'))}, 'fps': Value('float64')}, 'timestamp': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'episode_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'frame_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'next.reward': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'next.done': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'task_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}}, 'data_files_size_in_mb': Value('int64'), 'video_files_size_in_mb': Value('int64'), 'tsfile_conversion': {'data_format': Value('string'), 'note': Value('string'), 'tsfile_table_name': Value('string'), 'time_column': Value('string'), 'time_unit': Value('string'), 'time_definition': Value('string'), 'tag_columns': List(Value('string')), 'tag_definition': Value('string'), 'column_mapping': {'observation.state[8]': List(Value('string')), 'action[7]': List(Value('string')), 'timestamp': Value('string'), 'next.reward': Value('string'), 'next.done': Value('string'), 'index': Value('string'), 'observation.images.image': Value('string'), 'observation.images.wrist_image': Value('string')}}}
because column names don't match
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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              action: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
                child 0, min: list<item: double>
                    child 0, item: double
                child 1, max: list<item: double>
                    child 0, item: double
                child 2, mean: list<item: double>
                    child 0, item: double
                child 3, std: list<item: double>
                    child 0, item: double
                child 4, count: list<item: int64>
                    child 0, item: int64
              timestamp: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
                child 0, min: list<item: double>
                    child 0, item: double
                child 1, max: list<item: double>
                    child 0, item: double
                child 2, mean: list<item: double>
                    child 0, item: double
                child 3, std: list<item: double>
                    child 0, item: double
                child 4, count: list<item: int64>
                    child 0, item: int64
              observation.state: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, std: list<item: d (... 33 chars omitted)
                child 0, min: list<item: double>
                    child 0, item: double
                child 1, max: list<item: double>
                    child 0, item: double
                child 2, mean: list<item: double>
                    child 0, item: double
                child 3, std: list<item: double>
                    child 0, item: double
                child 4, count: list<item: int64>
                    child 0, item: int64
              frame_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
                chi
              ...
              ld 5, episode_index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                    child 3, fps: double
                child 6, frame_index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                    child 3, fps: double
                child 7, next.reward: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                    child 3, fps: double
                child 8, next.done: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                    child 3, fps: double
                child 9, index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                    child 3, fps: double
                child 10, task_index: struct<dtype: string, shape: list<item: int64>, names: null, fps: double>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                    child 3, fps: double
              to
              {'codebase_version': Value('string'), 'robot_type': Value('string'), 'total_episodes': Value('int64'), 'total_frames': Value('int64'), 'total_tasks': Value('int64'), 'chunks_size': Value('int64'), 'fps': Value('int64'), 'splits': {'train': Value('string')}, 'data_path': Value('string'), 'video_path': Value('string'), 'features': {'observation.images.image': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': List(Value('string')), 'video_info': {'video.fps': Value('float64'), 'video.codec': Value('string'), 'video.pix_fmt': Value('string'), 'video.is_depth_map': Value('bool'), 'has_audio': Value('bool')}}, 'observation.images.wrist_image': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': List(Value('string')), 'video_info': {'video.fps': Value('float64'), 'video.codec': Value('string'), 'video.pix_fmt': Value('string'), 'video.is_depth_map': Value('bool'), 'has_audio': Value('bool')}}, 'observation.state': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': {'motors': List(Value('string'))}, 'fps': Value('float64')}, 'action': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': {'motors': List(Value('string'))}, 'fps': Value('float64')}, 'timestamp': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'episode_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'frame_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'next.reward': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'next.done': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}, 'task_index': {'dtype': Value('string'), 'shape': List(Value('int64')), 'names': Value('null'), 'fps': Value('float64')}}, 'data_files_size_in_mb': Value('int64'), 'video_files_size_in_mb': Value('int64'), 'tsfile_conversion': {'data_format': Value('string'), 'note': Value('string'), 'tsfile_table_name': Value('string'), 'time_column': Value('string'), 'time_unit': Value('string'), 'time_definition': Value('string'), 'tag_columns': List(Value('string')), 'tag_definition': Value('string'), 'column_mapping': {'observation.state[8]': List(Value('string')), 'action[7]': List(Value('string')), 'timestamp': Value('string'), 'next.reward': Value('string'), 'next.done': Value('string'), 'index': Value('string'), 'observation.images.image': Value('string'), 'observation.images.wrist_image': Value('string')}}}
              because column names don't match

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Stanford HYDRA (LeRobot) — TsFile

This dataset converts the numeric time-series of the HuggingFace LeRobot dataset lerobot/stanford_hydra_dataset losslessly to the Apache TsFile format, and keeps the original camera videos alongside.

Original dataset

Source dataset: lerobot/stanford_hydra_dataset
Format: LeRobot v3
License: MIT
Content: a robot-manipulation dataset — 570 episodes / 358,234 frames / 10 fps, with proprioceptive state, actions, rewards, and two RGB camera streams.

What is in this repository

data/
└── stanford_hydra.tsfile          # numeric time-series (converted)
videos/
├── observation.images.image/...   # main camera (MP4, copied verbatim)
└── observation.images.wrist_image/...  # wrist camera (MP4, copied verbatim)

data/stanford_hydra.tsfile — the converted numeric time-series (state / action / reward / indices).
videos/… — the two camera MP4 streams, copied verbatim from the source (not converted; TsFile is a numeric time-series format and does not store video).

TsFile storage mapping (table model)

Role	Column(s)	Type	Notes
TAG	`episode_id`	STRING	`episode_{episode_index}`, 570 devices (one per episode)
Time	`frame_index * 100 ms`	INT64 (ms)	10 fps; frame_index restarts at 0 each episode and is strictly increasing
FIELD	`state_0` … `state_7`	FLOAT	`observation.state[8]` expanded
FIELD	`action_0` … `action_6`	FLOAT	`action[7]` expanded
FIELD	`episode_index`, `frame_index`, `sample_index`, `task_index`	INT64	indices (`index` → `sample_index`)
FIELD	`episode_timestamp_s`, `next_reward`	FLOAT	(`timestamp` → `episode_timestamp_s`, `next.reward` → `next_reward`)
FIELD	`next_done`	BOOLEAN	(`next.done`)

Conversion notes

Only the numeric time-series is converted. The observation.images.* features are dtype=video in LeRobot — their pixels live in the MP4 files under videos/, which are kept verbatim here.
TAG = episode_id (570 devices). Time = frame_index × 100 ms. Because frame_index restarts at 0 within each episode and is strictly increasing, every device's time axis is strictly increasing — no de-duplication or offset needed.
Array columns expanded: observation.state[8] → state_0..state_7, action[7] → action_0..action_6 (all FLOAT, matching the source float32).
Column names with dots are made TsFile-safe (next.reward → next_reward, …).
No columns dropped, no rows dropped: all 358,234 frames preserved.

Usage

from tsfile import TsFileReader

reader = TsFileReader("data/stanford_hydra.tsfile")
schemas = reader.get_all_table_schemas()
tname = next(iter(schemas))

cols = ["episode_id", "state_0", "action_0", "next_reward", "task_index"]
with reader.query_table(tname, cols, batch_size=65536) as rs:
    while (batch := rs.read_arrow_batch()) is not None:
        df = batch.to_pandas()
        # ... process ...
reader.close()

Citation

@misc{stanford_hydra_lerobot,
  title  = {Stanford HYDRA dataset (LeRobot)},
  author = {LeRobot team},
  url    = {https://huggingface.co/datasets/lerobot/stanford_hydra_dataset},
  publisher = {Hugging Face}
}

Original dataset licensed under MIT.

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Total file size:

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