Dataset Preview
Duplicate
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 1 new columns ({'stats'}) and 4 missing columns ({'task_index', 'task', 'episode_id', 'length'}).

This happened while the json dataset builder was generating data using

hf://datasets/xenorobotics/towel-fold-trimmed-v38/meta/episodes_stats.jsonl (at revision 87c28743b91c821b43ba98d312c10183a8b7e772)

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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              episode_index: int64
              stats: struct<observation.state: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, action: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, timestamp: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, frame_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, episode_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, task_index: struct<max: list<item: int64>, min: list<item: int64>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>, observation.images.front: struct<min: list<item: list<item: list<item: double>>>, max: list<item: list<item: list<item: double>>>, mean: list<item: list<item: list<item: double>>>, std: list<item: list<item: list<item: double>>>, count: list<item: int64>>>
                child 0, observation.state: struct<max: list<item: double>, min: list<item: double>, mean: list<item: double>, std: list<item: double>, count: list<item: int64>>
                    child 0, max: li
              ...
              : list<item: int64>
                        child 0, item: int64
                    child 1, min: list<item: int64>
                        child 0, item: int64
                    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
                child 7, observation.images.front: struct<min: list<item: list<item: list<item: double>>>, max: list<item: list<item: list<item: double>>>, mean: list<item: list<item: list<item: double>>>, std: list<item: list<item: list<item: double>>>, count: list<item: int64>>
                    child 0, min: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: list<item: double>
                                child 0, item: double
                    child 1, max: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: list<item: double>
                                child 0, item: double
                    child 2, mean: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: list<item: double>
                                child 0, item: double
                    child 3, std: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: list<item: double>
                                child 0, item: double
                    child 4, count: list<item: int64>
                        child 0, item: int64
              to
              {'episode_index': Value('int64'), 'episode_id': Value('string'), 'length': Value('int64'), 'task_index': Value('int64'), 'task': Value('string')}
              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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, 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 1702, 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 1833, 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 1 new columns ({'stats'}) and 4 missing columns ({'task_index', 'task', 'episode_id', 'length'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/xenorobotics/towel-fold-trimmed-v38/meta/episodes_stats.jsonl (at revision 87c28743b91c821b43ba98d312c10183a8b7e772)
              
              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
episode_id
string
length
int64
task_index
int64
task
string
0
episode_000000
313
0
towel_folding_first_fold
1
episode_000001
465
0
towel_folding_first_fold
2
episode_000002
513
0
towel_folding_first_fold
3
episode_000003
461
0
towel_folding_first_fold
0
episode_000000
313
0
towel_folding_first_fold
1
episode_000001
465
0
towel_folding_first_fold
2
episode_000002
513
0
towel_folding_first_fold
3
episode_000003
461
0
towel_folding_first_fold
0
null
null
null
null
1
null
null
null
null
2
null
null
null
null
3
null
null
null
null
4
null
null
null
null
5
null
null
null
null
6
null
null
null
null
7
null
null
null
null
8
null
null
null
null
9
null
null
null
null
10
null
null
null
null
11
null
null
null
null
12
null
null
null
null
13
null
null
null
null
14
null
null
null
null
15
null
null
null
null
16
null
null
null
null
17
null
null
null
null
18
null
null
null
null
19
null
null
null
null
20
null
null
null
null
21
null
null
null
null
22
null
null
null
null
23
null
null
null
null
24
null
null
null
null
25
null
null
null
null
26
null
null
null
null
27
null
null
null
null
28
null
null
null
null
29
null
null
null
null
30
null
null
null
null
31
null
null
null
null
32
null
null
null
null
33
null
null
null
null
34
null
null
null
null
35
null
null
null
null
36
null
null
null
null
37
null
null
null
null
38
null
null
null
null
39
null
null
null
null
40
null
null
null
null
41
null
null
null
null
42
null
null
null
null
43
null
null
null
null
44
null
null
null
null
45
null
null
null
null
46
null
null
null
null
47
null
null
null
null
48
null
null
null
null
49
null
null
null
null
50
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
0
towel_folding_first_fold
null
null
null
0
towel_folding_first_fold
null
null
null
null
null

Dataset Trimming Information

This dataset has been trimmed to the first fold detection point.

  • Original Dataset: arsenxeno/record-test
  • Trimmed Episodes: 4
  • Average Trimmed Length: 438 frames
  • Average Fold Time: 29.1 seconds

record-test

This dataset was generated using phosphobot.

This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot.

To get started in robotics, get your own phospho starter pack..

Downloads last month
5