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Ming0425
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lawam-actionvae-datasets

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
list<item: uint8>
to
List(Value('uint8'), length=36)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 87, in _generate_tables
                  yield Key(file_idx, batch_idx), cast_table_to_features(pa_table, self.info.features)
                                                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2285, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              list<item: uint8>
              to
              List(Value('uint8'), length=36)
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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arm_gripper_mask array 2D	arm_pose_mask array 2D	control_hz float32	controller_id string	current_state list	current_state_mask list	dual_arm_pose_end list	dual_arm_pose_start list	e39_mean list	e39_valid uint8	embodiment_id string	end_step int32	episode_id int64	episode_seed int64	group_id string	native_action_mask array 2D	native_actions array 2D	physical_anchor_mask array 2D	physical_anchors array 2D	sample_id string	source_commit string	source_id string	source_sha256 string	split string	start_step int32	task_id string	time_mask list	waypoints array 2D	window_sha256 string
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ActionVAE processed caches and sidecars

This repository contains the compact processed artifacts required by the ActionVAE and phased LaWAM training pipelines:

paired LIBERO z-effect/z-action teacher caches and trajectory targets;
RoboTwin paired teacher caches;
two RoboTwin ActionAE sidecar protocols;
public compressed LARYBench pilot archives used for offline validation.

The full LIBERO and RoboTwin raw/merged datasets are not duplicated here. Use migration/scripts/restore_public_datasets.sh from the companion source repository to restore them from pinned public revisions.

Licenses and provenance

The caches and sidecars are derived from public LIBERO and RoboTwin research datasets; the upstream dataset terms continue to apply. The LARYBench pilot archives retain their upstream provenance and terms. This repository does not grant additional rights over the underlying demonstrations.

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