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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 14 missing columns ({'dataset_from_index', 'data/file_index', 'videos/observation.images.wrist/file_index', 'videos/observation.images.front/from_timestamp', 'length', 'dataset_to_index', 'videos/observation.images.wrist/chunk_index', 'videos/observation.images.wrist/to_timestamp', 'tasks', 'videos/observation.images.front/to_timestamp', 'videos/observation.images.front/chunk_index', 'videos/observation.images.wrist/from_timestamp', 'data/chunk_index', 'videos/observation.images.front/file_index'}).

This happened while the json dataset builder was generating data using

hf://datasets/SunyGala/leisaac-pick-orange/meta/episodes_stats.jsonl (at revision 69bd56d3fc567f354367a81f04bc71a59b65247d), [/tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/episodes.jsonl (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/episodes.jsonl), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/episodes_stats.jsonl (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/episodes_stats.jsonl), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/info.json (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/info.json), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/sarm_annotations_sparse.json (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/sarm_annotations_sparse.json), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/stats.json (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/stats.json), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/tasks.jsonl (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/tasks.jsonl), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/temporal_proportions_sparse.json (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/temporal_proportions_sparse.json)]

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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/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<action: struct<min: list<item: double>, max: list<item: double>, mean: list<item: double>, st (... 1442 chars omitted)
                child 0, 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
                child 1, 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
                child 2, observation.images.front: struct<min: list<item: list<item: list<item: double>>>, max: list<item: list<item: list<item: double (... 129 chars omitted)
                    child 0, min: list<item: list<item: list<item: double>>>
                        child 0, item: list<item: list<item: double>>
                            child 0, item: list<item: double>
                      
              ...
              int64
                child 6, episode_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
                    child 0, min: list<item: int64>
                        child 0, item: int64
                    child 1, max: 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, index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
                    child 0, min: list<item: int64>
                        child 0, item: int64
                    child 1, max: 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 8, task_index: struct<min: list<item: int64>, max: list<item: int64>, mean: list<item: double>, std: list<item: dou (... 31 chars omitted)
                    child 0, min: list<item: int64>
                        child 0, item: int64
                    child 1, max: 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
              to
              {'episode_index': Value('int64'), 'data/chunk_index': Value('int64'), 'data/file_index': Value('int64'), 'dataset_from_index': Value('int64'), 'dataset_to_index': Value('int64'), 'videos/observation.images.front/chunk_index': Value('int64'), 'videos/observation.images.front/file_index': Value('int64'), 'videos/observation.images.front/from_timestamp': Value('float64'), 'videos/observation.images.front/to_timestamp': Value('float64'), 'videos/observation.images.wrist/chunk_index': Value('int64'), 'videos/observation.images.wrist/file_index': Value('int64'), 'videos/observation.images.wrist/from_timestamp': Value('float64'), 'videos/observation.images.wrist/to_timestamp': Value('float64'), 'tasks': List(Value('string')), 'length': Value('int64')}
              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 1347, 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 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, 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 14 missing columns ({'dataset_from_index', 'data/file_index', 'videos/observation.images.wrist/file_index', 'videos/observation.images.front/from_timestamp', 'length', 'dataset_to_index', 'videos/observation.images.wrist/chunk_index', 'videos/observation.images.wrist/to_timestamp', 'tasks', 'videos/observation.images.front/to_timestamp', 'videos/observation.images.front/chunk_index', 'videos/observation.images.wrist/from_timestamp', 'data/chunk_index', 'videos/observation.images.front/file_index'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/SunyGala/leisaac-pick-orange/meta/episodes_stats.jsonl (at revision 69bd56d3fc567f354367a81f04bc71a59b65247d), [/tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/episodes.jsonl (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/episodes.jsonl), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/episodes_stats.jsonl (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/episodes_stats.jsonl), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/info.json (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/info.json), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/sarm_annotations_sparse.json (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/sarm_annotations_sparse.json), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/stats.json (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/stats.json), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/tasks.jsonl (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/tasks.jsonl), /tmp/hf-datasets-cache/medium/datasets/48634921444683-config-parquet-and-info-SunyGala-leisaac-pick-ora-3242a98a/hub/datasets--SunyGala--leisaac-pick-orange/snapshots/69bd56d3fc567f354367a81f04bc71a59b65247d/meta/temporal_proportions_sparse.json (origin=hf://datasets/SunyGala/leisaac-pick-orange@69bd56d3fc567f354367a81f04bc71a59b65247d/meta/temporal_proportions_sparse.json)]
              
              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
data/chunk_index
int64
data/file_index
int64
dataset_from_index
int64
dataset_to_index
int64
videos/observation.images.front/chunk_index
int64
videos/observation.images.front/file_index
int64
videos/observation.images.front/from_timestamp
float64
videos/observation.images.front/to_timestamp
float64
videos/observation.images.wrist/chunk_index
int64
videos/observation.images.wrist/file_index
int64
videos/observation.images.wrist/from_timestamp
float64
videos/observation.images.wrist/to_timestamp
float64
tasks
list
length
int64
0
0
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0
774
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0
25.8
[ "Grab orange and place into plate" ]
774
1
0
0
774
1,326
0
0
25.8
44.2
0
0
25.8
44.2
[ "Grab orange and place into plate" ]
552
2
0
0
1,326
1,935
0
0
44.2
64.5
0
0
44.2
64.5
[ "Grab orange and place into plate" ]
609
3
0
0
1,935
2,604
0
0
64.5
86.8
0
0
64.5
86.8
[ "Grab orange and place into plate" ]
669
4
0
0
2,604
3,216
0
0
86.8
107.2
0
0
86.8
107.2
[ "Grab orange and place into plate" ]
612
5
0
0
3,216
3,769
0
0
107.2
125.633333
0
0
107.2
125.633333
[ "Grab orange and place into plate" ]
553
6
0
0
3,769
4,484
0
0
125.633333
149.466667
0
0
125.633333
149.466667
[ "Grab orange and place into plate" ]
715
7
0
0
4,484
5,018
0
0
149.466667
167.266667
0
0
149.466667
167.266667
[ "Grab orange and place into plate" ]
534
8
0
0
5,018
5,467
0
0
167.266667
182.233333
0
0
167.266667
182.233333
[ "Grab orange and place into plate" ]
449
9
0
0
5,467
5,912
0
0
182.233333
197.066667
0
0
182.233333
197.066667
[ "Grab orange and place into plate" ]
445
10
0
0
5,912
6,405
0
0
197.066667
213.5
0
0
197.066667
213.5
[ "Grab orange and place into plate" ]
493
11
0
0
6,405
6,890
0
0
213.5
229.666667
0
0
213.5
229.666667
[ "Grab orange and place into plate" ]
485
12
0
0
6,890
7,691
0
0
229.666667
256.366667
0
0
229.666667
256.366667
[ "Grab orange and place into plate" ]
801
13
0
0
7,691
8,317
0
0
256.366667
277.233333
0
0
256.366667
277.233333
[ "Grab orange and place into plate" ]
626
14
0
0
8,317
8,844
0
0
277.233333
294.8
0
0
277.233333
294.8
[ "Grab orange and place into plate" ]
527
15
0
0
8,844
9,592
0
0
294.8
319.733333
0
0
294.8
319.733333
[ "Grab orange and place into plate" ]
748
16
0
0
9,592
10,415
0
0
319.733333
347.166667
0
0
319.733333
347.166667
[ "Grab orange and place into plate" ]
823
17
0
0
10,415
10,973
0
0
347.166667
365.766667
0
0
347.166667
365.766667
[ "Grab orange and place into plate" ]
558
18
0
0
10,973
11,789
0
0
365.766667
392.966667
0
0
365.766667
392.966667
[ "Grab orange and place into plate" ]
816
19
0
0
11,789
12,682
0
0
392.966667
422.733333
0
0
392.966667
422.733333
[ "Grab orange and place into plate" ]
893
20
0
0
12,682
13,369
0
0
422.733333
445.633333
0
0
422.733333
445.633333
[ "Grab orange and place into plate" ]
687
21
0
0
13,369
14,144
0
0
445.633333
471.466667
0
0
445.633333
471.466667
[ "Grab orange and place into plate" ]
775
22
0
0
14,144
14,540
0
0
471.466667
484.666667
0
0
471.466667
484.666667
[ "Grab orange and place into plate" ]
396
23
0
0
14,540
15,378
0
0
484.666667
512.6
0
0
484.666667
512.6
[ "Grab orange and place into plate" ]
838
24
0
0
15,378
16,154
0
0
512.6
538.466667
0
0
512.6
538.466667
[ "Grab orange and place into plate" ]
776
25
0
0
16,154
16,687
0
0
538.466667
556.233333
0
0
538.466667
556.233333
[ "Grab orange and place into plate" ]
533
26
0
0
16,687
17,740
0
0
556.233333
591.333333
0
0
556.233333
591.333333
[ "Grab orange and place into plate" ]
1,053
27
0
0
17,740
18,331
0
0
591.333333
611.033333
0
0
591.333333
611.033333
[ "Grab orange and place into plate" ]
591
28
0
0
18,331
18,859
0
0
611.033333
628.633333
0
0
611.033333
628.633333
[ "Grab orange and place into plate" ]
528
29
0
0
18,859
19,541
0
1
0
22.733333
0
0
628.633333
651.366667
[ "Grab orange and place into plate" ]
682
30
0
0
19,541
20,396
0
1
22.733333
51.233333
0
0
651.366667
679.866667
[ "Grab orange and place into plate" ]
855
31
0
0
20,396
20,852
0
1
51.233333
66.433333
0
0
679.866667
695.066667
[ "Grab orange and place into plate" ]
456
32
0
0
20,852
21,352
0
1
66.433333
83.1
0
0
695.066667
711.733333
[ "Grab orange and place into plate" ]
500
33
0
0
21,352
22,075
0
1
83.1
107.2
0
0
711.733333
735.833333
[ "Grab orange and place into plate" ]
723
34
0
0
22,075
22,842
0
1
107.2
132.766667
0
0
735.833333
761.4
[ "Grab orange and place into plate" ]
767
35
0
0
22,842
23,243
0
1
132.766667
146.133333
0
0
761.4
774.766667
[ "Grab orange and place into plate" ]
401
36
0
0
23,243
23,663
0
1
146.133333
160.133333
0
0
774.766667
788.766667
[ "Grab orange and place into plate" ]
420
37
0
0
23,663
24,363
0
1
160.133333
183.466667
0
0
788.766667
812.1
[ "Grab orange and place into plate" ]
700
38
0
0
24,363
24,883
0
1
183.466667
200.8
0
0
812.1
829.433333
[ "Grab orange and place into plate" ]
520
39
0
0
24,883
25,414
0
1
200.8
218.5
0
0
829.433333
847.133333
[ "Grab orange and place into plate" ]
531
40
0
0
25,414
25,834
0
1
218.5
232.5
0
1
0
14
[ "Grab orange and place into plate" ]
420
41
0
0
25,834
26,238
0
1
232.5
245.966667
0
1
14
27.466667
[ "Grab orange and place into plate" ]
404
42
0
0
26,238
26,656
0
1
245.966667
259.9
0
1
27.466667
41.4
[ "Grab orange and place into plate" ]
418
43
0
0
26,656
27,349
0
1
259.9
283
0
1
41.4
64.5
[ "Grab orange and place into plate" ]
693
44
0
0
27,349
27,793
0
1
283
297.8
0
1
64.5
79.3
[ "Grab orange and place into plate" ]
444
45
0
0
27,793
28,717
0
1
297.8
328.6
0
1
79.3
110.1
[ "Grab orange and place into plate" ]
924
46
0
0
28,717
29,488
0
1
328.6
354.3
0
1
110.1
135.8
[ "Grab orange and place into plate" ]
771
47
0
0
29,488
30,022
0
1
354.3
372.1
0
1
135.8
153.6
[ "Grab orange and place into plate" ]
534
48
0
0
30,022
30,403
0
1
372.1
384.8
0
1
153.6
166.3
[ "Grab orange and place into plate" ]
381
49
0
0
30,403
30,992
0
1
384.8
404.433333
0
1
166.3
185.933333
[ "Grab orange and place into plate" ]
589
50
0
0
30,992
31,420
0
1
404.433333
418.7
0
1
185.933333
200.2
[ "Grab orange and place into plate" ]
428
51
0
0
31,420
31,968
0
1
418.7
436.966667
0
1
200.2
218.466667
[ "Grab orange and place into plate" ]
548
52
0
0
31,968
32,443
0
1
436.966667
452.8
0
1
218.466667
234.3
[ "Grab orange and place into plate" ]
475
53
0
0
32,443
32,792
0
1
452.8
464.433333
0
1
234.3
245.933333
[ "Grab orange and place into plate" ]
349
54
0
0
32,792
33,264
0
1
464.433333
480.166667
0
1
245.933333
261.666667
[ "Grab orange and place into plate" ]
472
55
0
0
33,264
33,726
0
1
480.166667
495.566667
0
1
261.666667
277.066667
[ "Grab orange and place into plate" ]
462
56
0
0
33,726
34,187
0
1
495.566667
510.933333
0
1
277.066667
292.433333
[ "Grab orange and place into plate" ]
461
57
0
0
34,187
34,794
0
1
510.933333
531.166667
0
1
292.433333
312.666667
[ "Grab orange and place into plate" ]
607
58
0
0
34,794
35,783
0
1
531.166667
564.133333
0
1
312.666667
345.633333
[ "Grab orange and place into plate" ]
989
59
0
0
35,783
36,293
0
1
564.133333
581.133333
0
1
345.633333
362.633333
[ "Grab orange and place into plate" ]
510
0
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End of preview.

LeIsaac Pick Orange Dataset

A robot manipulation dataset for learning to pick and place oranges, collected in Isaac Lab simulation environment.

Task Description

The robot performs the following subtasks:

  1. Approach orange - Move gripper towards the orange
  2. Grab orange - Close gripper to grasp the orange
  3. Move to plate - Transport the orange towards the plate
  4. Place orange - Open gripper to release the orange on the plate
  5. Retreat - Move away from the placement location

SARM Annotations

This dataset includes Stage-Aware Reward Modeling (SARM) sparse annotations for all 60 episodes. Each episode is annotated with temporal boundaries for each subtask.

Temporal Proportions (Average)

Subtask Proportion
approach orange 17.4%
grab orange 11.3%
move to plate 17.9%
place orange 20.9%
retreat 32.5%

Annotation Files

  • meta/sarm_annotations_sparse.json - JSON format annotations
  • meta/temporal_proportions_sparse.json - Average temporal proportions
  • Episode-level annotations in parquet files with columns:
    • sparse_subtask_names
    • sparse_subtask_start_times
    • sparse_subtask_end_times
    • sparse_subtask_start_frames
    • sparse_subtask_end_frames

Dataset Structure

leisaac-pick-orange/
β”œβ”€β”€ meta/
β”‚   β”œβ”€β”€ info.json
β”‚   β”œβ”€β”€ stats.json
β”‚   β”œβ”€β”€ tasks.parquet
β”‚   β”œβ”€β”€ episodes/
β”‚   β”‚   └── chunk-000/
β”‚   β”‚       └── file-*.parquet
β”‚   β”œβ”€β”€ sarm_annotations_sparse.json
β”‚   └── temporal_proportions_sparse.json
β”œβ”€β”€ data/
β”‚   └── chunk-000/
β”‚       └── file-*.parquet
└── videos/
    β”œβ”€β”€ observation.images.front/
    β”‚   └── chunk-000/
    β”‚       └── file-*.mp4
    └── observation.images.wrist/
        └── chunk-000/
            └── file-*.mp4

Usage

from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

# Load the dataset
dataset = LeRobotDataset("SunyGala/leisaac-pick-orange")

# Access episodes
for i in range(len(dataset)):
    episode = dataset[i]
    # episode contains observation images, robot state, and actions

License

Apache 2.0

Citation

If you use this dataset, please cite the relevant works:

@software{lerobot2024,
  title = {LeRobot: Making AI for Robotics more accessible},
  author = {LeRobot team},
  url = {https://github.com/huggingface/lerobot}
}

Acknowledgments

  • Dataset generated using Isaac Lab
  • Annotations generated using SiliconFlow API with Qwen3-VL model
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