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Datasets:
lyang116
/
STDB-CC-datasets

Tasks:
Robotics
License:
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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:    CastError
Message:      Couldn't cast
task_name: string
episode_id: int64
pair_group_id: string
canonical_info_source: string
scene_id: int64
scene_seed: int64
trajectory_seed: int64
data_path: string
video_path: string
instruction_path: string
traj_path: string
scene_info_path: string
canonical_info: struct<{A}: string, {a}: string>
  child 0, {A}: string
  child 1, {a}: string
observed_info: struct<{A}: string, {a}: string>
  child 0, {A}: string
  child 1, {a}: string
is_alignment_valid: bool
split: string
to
{'scene_id': Value('int64'), 'episode_id': Value('int64'), 'pair_group_id': Value('string'), 'data_path': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              task_name: string
              episode_id: int64
              pair_group_id: string
              canonical_info_source: string
              scene_id: int64
              scene_seed: int64
              trajectory_seed: int64
              data_path: string
              video_path: string
              instruction_path: string
              traj_path: string
              scene_info_path: string
              canonical_info: struct<{A}: string, {a}: string>
                child 0, {A}: string
                child 1, {a}: string
              observed_info: struct<{A}: string, {a}: string>
                child 0, {A}: string
                child 1, {a}: string
              is_alignment_valid: bool
              split: string
              to
              {'scene_id': Value('int64'), 'episode_id': Value('int64'), 'pair_group_id': Value('string'), 'data_path': Value('string')}
              because column names don't match

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YAML Metadata Warning:The task_categories "imitation-learning" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

STDB-CC Datasets

STDB-CC is a RoboTwin-based robot imitation learning dataset for studying visual distribution shift and task/environment factorization.

The dataset uses one fixed split:

scene0 + scene3 -> train
scene2          -> validation
scene1          -> test

Split token:

scene03_train_scene2_val_scene1_test

Directory Layout 

raw/robotwin_stdb_cc/
  <task_name>/
    stdb_cc_scene0/
    stdb_cc_scene1/
    stdb_cc_scene2/
    stdb_cc_scene3/
    manifests/scene03_train_scene2_val_scene1_test/

processed/diffusion_policy/
  scene03_train_scene2_val_scene1_test/
    <task_name>-stdb_cc_task-50.zarr/

metadata/
  dataset_summary.json
  verify_reports/

Contents

  • raw/robotwin_stdb_cc/: raw RoboTwin STDB-CC data with 4 visual scenes per task.
  • processed/diffusion_policy/: Diffusion Policy zarr datasets built from the train split only.
  • metadata/verify_reports/: local verification reports for the raw task data.
  • metadata/dataset_summary.json: machine-readable task list, split information, and artifact counts.

Current Release

  • Tasks: 50
  • Episodes per task per scene: 50
  • Raw episodes per task: 200
  • DP train episodes per task: 100
  • Scenes: stdb_cc_scene0, stdb_cc_scene1, stdb_cc_scene2, stdb_cc_scene3

The raw task manifests are stored under each task:

raw/robotwin_stdb_cc/<task_name>/manifests/scene03_train_scene2_val_scene1_test/

The DP zarr artifacts embed their train manifest as:

processed/diffusion_policy/scene03_train_scene2_val_scene1_test/<task_name>-stdb_cc_task-50.zarr/stdb_cc_manifest.jsonl

Notes

This repository currently contains the raw RoboTwin STDB-CC data and the Diffusion Policy conversion. Future conversions for other policy families can be added under processed/<format_or_model>/ without changing the raw data layout.
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