The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 13 new columns ({'codebase_version', 'fps', 'total_videos', 'total_frames', 'total_tasks', 'features', 'robot_type', 'chunks_size', 'splits', 'video_path', 'data_path', 'total_episodes', 'total_chunks'}) and 3 missing columns ({'episode_index', 'length', 'tasks'}).

This happened while the json dataset builder was generating data using

hf://datasets/phospho-ai/bobololo/info.json (at revision 79fec651a75a3e4ac4f56a4c325bbe9885c9e92e)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              codebase_version: string
              robot_type: string
              total_episodes: int64
              total_frames: int64
              total_tasks: int64
              total_videos: int64
              total_chunks: int64
              chunks_size: int64
              fps: int64
              splits: struct<train: string>
                child 0, train: string
              data_path: string
              video_path: string
              features: struct<observation.image: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, video_info: struct<video.fps: int64, video.codec: string, video.pix_fmt: string, video.is_depth_map: bool, has_audio: bool>>, observation.state: struct<dtype: string, shape: list<item: int64>, names: struct<motors: list<item: string>>>, action: struct<dtype: string, shape: list<item: int64>, names: struct<motors: list<item: string>>>, observation.action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>, episode_index: struct<dtype: string, shape: list<item: int64>, names: null>, frame_index: struct<dtype: string, shape: list<item: int64>, names: null>, timestamp: struct<dtype: string, shape: list<item: int64>, names: null>, next.reward: struct<dtype: string, shape: list<item: int64>, names: null>, next.done: struct<dtype: string, shape: list<item: int64>, names: null>, next.success: struct<dtype: string, shape: list<item: int64>, names: null>, index: struct<dtype: string, shape: list<item: int64>, names: null>, task_index: struct<dtype: string, shape: list<item: int64>, names: null>>
                child 0, observation.image: struct<dtype: string, shape: list<item: int64>, names: list<item: s
              ...
                  child 2, names: null
                child 5, frame_index: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 6, timestamp: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 7, next.reward: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 8, next.done: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 9, next.success: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 10, index: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 11, task_index: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
              to
              {'episode_index': Value(dtype='int64', id=None), 'tasks': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'length': Value(dtype='int64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 13 new columns ({'codebase_version', 'fps', 'total_videos', 'total_frames', 'total_tasks', 'features', 'robot_type', 'chunks_size', 'splits', 'video_path', 'data_path', 'total_episodes', 'total_chunks'}) and 3 missing columns ({'episode_index', 'length', 'tasks'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/phospho-ai/bobololo/info.json (at revision 79fec651a75a3e4ac4f56a4c325bbe9885c9e92e)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

episode_index
int64
tasks
sequence
length
int64
codebase_version
string
robot_type
string
total_episodes
int64
total_frames
int64
total_tasks
int64
total_videos
int64
total_chunks
int64
chunks_size
int64
fps
int64
splits
dict
data_path
string
video_path
string
features
dict
observation.state
sequence
observation.joints_position
sequence
observation.image
sequence
next.reward
int64
next.done
float64
next.success
float64
timestamp
float64
0
[ null ]
19
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
v2.0
so-100
1
19
1
1
1
1,000
10
{ "train": "0:206" }
data/episode_{episode_index:06d}.parquet
videos/{video_key}/episode_{episode_index:06d}.mp4
{ "observation.image": { "dtype": "video", "shape": [ 224, 224, 3 ], "names": [ "height", "width", "channel" ], "video_info": { "video.fps": 10, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "motor_0", "motor_1", "motor_2", "motor_3", "motor_4", "motor_5" ] } }, "action": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "motor_0", "motor_1", "motor_2", "motor_3", "motor_4", "motor_5" ] } }, "observation.action": { "dtype": "float32", "shape": [ 7 ], "names": [ "x", "y", "z", "rx", "ry", "rz", "gripper" ] }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "next.reward": { "dtype": "float32", "shape": [ 1 ], "names": null }, "next.done": { "dtype": "bool", "shape": [ 1 ], "names": null }, "next.success": { "dtype": "bool", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } }
null
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[0.36274477839469904,0.025095846503973,0.228017330169677,0.03573540155339,0.067881497247121,2.871816(...TRUNCATED)
[0.015496049358753,0.379697650905781,-0.354303197897956,0.07218178988276601,0.015095044927927001,1.4(...TRUNCATED)
[[[91.0,102.0,111.0],[91.0,102.0,111.0],[92.0,103.0,111.0],[92.0,104.0,113.0],[94.0,105.0,114.0],[95(...TRUNCATED)
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[0.36122296515264,0.023405455543022,0.225509375333786,-0.037000817274891004,0.049563681274915006,2.8(...TRUNCATED)
[0.010161662226542,0.366956194692842,-0.374039629668141,-0.021693204443779,-0.02320678925472,1.47902(...TRUNCATED)
[[[89.05263157894737,100.84210526315789,108.89473684210526],[89.84210526315789,101.21052631578948,10(...TRUNCATED)
0
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[0.359442472457885,0.022078081965446,0.21177899837493802,-0.050908946535130006,-0.044982477826564005(...TRUNCATED)
[0.0059536928031500005,0.348738256377425,-0.38750822518954003,-0.039946763801139006,-0.0325681381913(...TRUNCATED)
[[[87.0,100.0,107.0],[89.0,100.0,107.0],[90.0,101.0,109.0],[91.0,101.0,110.0],[91.0,102.0,111.0],[92(...TRUNCATED)
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[0.00102347406708,0.0009743924954460001,0.004350440048415,0.022886437139734,0.031187343237768003,0.0(...TRUNCATED)
[0.0030837381097380003,0.009896612828052001,0.010742139599822,0.03207757943069,0.012511822156119,0.0(...TRUNCATED)
[[[0.9444399181540191,0.5860804592452651,1.25214497403898],[0.5860804592452651,0.6137844099837151,1.(...TRUNCATED)
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