Datasets:

rangeryx2005
/

worldarena-robotwin-clean1500

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
video: string
prompt: string
action_path: string
intrinsic_path: string
extrinsic_path: string
original_size: list<item: int64>
  child 0, item: int64
episode_id: string
caption_source: string
caption_quality_flags: string
quality_flags: string
action_map_convention: string
action_map_convention_path: string
action_map_camera_name: string
action_map_intrinsic_mode: string
action_map_extrinsic_mode: string
action_map_quat_order: string
quat_order: string
ee_local_z_offset: double
camera_source: string
worldarena_camera_policy: string
_path: string
camera_name: string
name: string
intrinsic_mode: string
abot_expected_quat_order: string
extrinsic_mode: string
notes: string
to
{'name': Value('string'), 'camera_name': Value('string'), 'intrinsic_mode': Value('string'), 'extrinsic_mode': Value('string'), 'ee_local_z_offset': Value('float64'), 'camera_source': Value('string'), 'worldarena_camera_policy': Value('string'), 'notes': Value('string'), 'quat_order': Value('string'), 'abot_expected_quat_order': Value('string'), '_path': 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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              video: string
              prompt: string
              action_path: string
              intrinsic_path: string
              extrinsic_path: string
              original_size: list<item: int64>
                child 0, item: int64
              episode_id: string
              caption_source: string
              caption_quality_flags: string
              quality_flags: string
              action_map_convention: string
              action_map_convention_path: string
              action_map_camera_name: string
              action_map_intrinsic_mode: string
              action_map_extrinsic_mode: string
              action_map_quat_order: string
              quat_order: string
              ee_local_z_offset: double
              camera_source: string
              worldarena_camera_policy: string
              _path: string
              camera_name: string
              name: string
              intrinsic_mode: string
              abot_expected_quat_order: string
              extrinsic_mode: string
              notes: string
              to
              {'name': Value('string'), 'camera_name': Value('string'), 'intrinsic_mode': Value('string'), 'extrinsic_mode': Value('string'), 'ee_local_z_offset': Value('float64'), 'camera_source': Value('string'), 'worldarena_camera_policy': Value('string'), 'notes': Value('string'), 'quat_order': Value('string'), 'abot_expected_quat_order': Value('string'), '_path': Value('string')}
              because column names don't match

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WorldArena Track1 RoboTwin Aloha-AgileX Clean 1500

This dataset is a curated 1,500-episode RoboTwin2/Aloha-AgileX dual-arm gripper dataset prepared for WorldArena Track1-style ABot-PhysWorld SFT and A2V experiments.

It contains only the cleaned release artifacts. The original RoboTwin raw collection folders, HDF5 dumps, collection logs, smoke-test outputs, camera-debug grids, and training checkpoints are intentionally excluded.

Summary

Episodes: 1500
Embodiment: Aloha-AgileX dual-arm gripper
Config: wa_clean_fixed, RT sample count 256 collection setting
Video: 640x480 mp4
First frame: 320x240 png
Actions: joint14, normalized joint14, ee16, and joint14+ee16
Camera: head camera, HDF5 verified for self-collected RoboTwin data
SFT positives: 1500
A2V positives: 1500
Captions: short, WorldArena-style, and ABot-style dense captions

Trajectory length T:

min: 76
median: 191.0
p95: 512.0
max: 715

Task Distribution

task_family	count	percent
articulated_open_close	130	8.7%
button_press_click	150	10.0%
coverage_unknown	100	6.7%
dumping_pouring	60	4.0%
handover	50	3.3%
hanging	40	2.7%
lifting	100	6.7%
object_to_container	170	11.3%
pick_place	180	12.0%
ranking_arrangement	60	4.0%
rotation_orientation	70	4.7%
scanning_qrcode	80	5.3%
shaking	70	4.7%
stacking	120	8.0%
tool_use	120	8.0%

Directory Structure

episodes/rt_xxxxxx/
  observation.mp4
  first_frame.png
  action_joint14_raw.npy
  action_joint14_norm.npy
  action_ee16.npy
  action_joint14_ee16.npy
  camera_intrinsic.json
  camera_extrinsic.json
  camera_info.json
  meta.json
  quick_contact_sheet.jpg
  visual_sanity.json

manifests/
  episode_manifest.parquet
  episode_manifest.csv
  action_normalization_config.json
  worldarena_target_spec.yaml
  collection_job_summary.csv

sft_worldarena_style_caption_mix/metadata.jsonl
sft_pilot/train.jsonl
sft_pilot/val.jsonl
sft_pilot/fixed_eval.jsonl
a2v_worldarena_ee16_caption_mix/metadata.jsonl
captions_abot_style/

Metadata Formats

SFT metadata lines:

{"video":"episodes/rt_000000/observation.mp4","prompt":"...","episode_id":"rt_000000"}

A2V metadata lines:

{"video":"episodes/rt_000000/observation.mp4","prompt":"...","action_path":"episodes/rt_000000/action_ee16.npy","intrinsic_path":"episodes/rt_000000/camera_intrinsic.json","extrinsic_path":"episodes/rt_000000/camera_extrinsic.json","original_size":[480,640]}

Action Representation

action_joint14_raw.npy: left arm 6 + left gripper 1 + right arm 6 + right gripper 1.
action_ee16.npy: left xyz + left quaternion + left gripper + right xyz + right quaternion + right gripper.
action_joint14_ee16.npy: concatenated 30D representation.
Quaternion convention in A2V metadata: wxyz.
EE local z offset used for action-map training/debug: 0.0.
Action-map convention: robotwin_hdf5_z0.

Camera Convention

Camera source distribution:

{
  "hdf5_verified": 1500
}

Embodiment distribution:

{
  "aloha-agilex": 1500
}

For self-collected RoboTwin data, the camera convention is:

camera: head_camera
intrinsic: raw OpenCV K
extrinsic: inverse of RoboTwin observation/head_camera/extrinsic_cv, exported as camera-to-world JSON for ABot/VACE utilities

Recommended Usage

SFT:

DATASET_BASE_PATH=/path/to/this_dataset
DATASET_METADATA_PATH=$DATASET_BASE_PATH/sft_worldarena_style_caption_mix/metadata.jsonl

A2V ee16:

DATASET_BASE_PATH=/path/to/this_dataset
DATASET_METADATA_PATH=$DATASET_BASE_PATH/a2v_worldarena_ee16_caption_mix/metadata.jsonl

The A2V metadata is configured for ee16 with quat_order=wxyz and ee_local_z_offset=0.0.

Notes and Limitations

This is a generated RoboTwin2-style dataset intended for WorldArena Track1 experiments, not official WorldArena training data.
The release excludes raw RoboTwin HDF5 and collection logs to keep the dataset compact.
The dataset is focused on Aloha-AgileX dual-arm gripper manipulation and is not intended as a cross-embodiment dataset.
Use the included manifests and camera/action metadata when training ABot-PhysWorld A2V models.

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