Dataset Viewer
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
codebase_version: string
robot_type: string
total_episodes: int64
total_frames: int64
total_tasks: int64
chunks_size: int64
data_files_size_in_mb: int64
video_files_size_in_mb: int64
fps: int64
splits: struct<train: string>
child 0, train: string
data_path: string
video_path: string
features: struct<observation.state: struct<dtype: string, shape: list<item: int64>, names: list<item: string>> (... 1032 chars omitted)
child 0, observation.state: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 1, action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 2, observation.images.front: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.height (... 157 chars omitted)
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 3, info: struct<video.height: int64, video.width: int64, video.codec: string, video.pix_fmt: string, video.is (... 75 chars omitted)
child 0, video.height: int64
chil
...
: null
child 11, pretrained_path: null
child 12, horizon: int64
child 13, n_action_steps: int64
child 14, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>
child 0, VISUAL: string
child 1, STATE: string
child 2, ACTION: string
child 15, drop_n_last_frames: int64
child 16, vision_backbone: string
child 17, crop_shape: list<item: int64>
child 0, item: int64
child 18, crop_is_random: bool
child 19, pretrained_backbone_weights: null
child 20, use_group_norm: bool
child 21, spatial_softmax_num_keypoints: int64
child 22, use_separate_rgb_encoder_per_camera: bool
child 23, down_dims: list<item: int64>
child 0, item: int64
child 24, kernel_size: int64
child 25, n_groups: int64
child 26, diffusion_step_embed_dim: int64
child 27, use_film_scale_modulation: bool
child 28, noise_scheduler_type: string
child 29, num_train_timesteps: int64
child 30, beta_schedule: string
child 31, beta_start: double
child 32, beta_end: double
child 33, prediction_type: string
child 34, clip_sample: bool
child 35, clip_sample_range: double
child 36, num_inference_steps: int64
child 37, do_mask_loss_for_padding: bool
child 38, optimizer_lr: double
child 39, optimizer_betas: list<item: double>
child 0, item: double
child 40, optimizer_eps: double
child 41, optimizer_weight_decay: double
child 42, scheduler_name: string
child 43, scheduler_warmup_steps: int64
seed: int64
steps: int64
to
{'dataset': {'repo_id': Value('string'), 'root': Value('string'), 'episodes': Value('null'), 'image_transforms': {'enable': Value('bool'), 'max_num_transforms': Value('int64'), 'random_order': Value('bool'), 'tfs': {'brightness': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'brightness': List(Value('float64'))}}, 'contrast': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'contrast': List(Value('float64'))}}, 'saturation': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'saturation': List(Value('float64'))}}, 'hue': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'hue': List(Value('float64'))}}, 'sharpness': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'sharpness': List(Value('float64'))}}, 'affine': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'degrees': List(Value('float64')), 'translate': List(Value('float64'))}}, 'blur': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'kernel_size': Value('int64'), 'sigma': List(Value('float64'))}}, 'erasing': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'p': Value('float64'), 'scale': List(Value('float64')), 'ratio': List(Value('float64'))}}}}, 'revision': Value('null'), 'use_imagenet_stats': Value('bool'), 'video_backend': Value('string'), 'streaming': Value('bool')}, 'env': Value('null'), 'policy': {'type': Value('string'), 'n_obs_steps': Value('int64'), 'input_features': {'observation.imag
...
eps': Value('int64'), 'do_mask_loss_for_padding': Value('bool'), 'optimizer_lr': Value('float64'), 'optimizer_betas': List(Value('float64')), 'optimizer_eps': Value('float64'), 'optimizer_weight_decay': Value('float64'), 'scheduler_name': Value('string'), 'scheduler_warmup_steps': Value('int64')}, 'output_dir': Value('string'), 'job_name': Value('string'), 'resume': Value('bool'), 'seed': Value('int64'), 'num_workers': Value('int64'), 'batch_size': Value('int64'), 'steps': Value('int64'), 'eval_freq': Value('int64'), 'log_freq': Value('int64'), 'tolerance_s': Value('float64'), 'save_checkpoint': Value('bool'), 'save_freq': Value('int64'), 'use_policy_training_preset': Value('bool'), 'optimizer': {'type': Value('string'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'grad_clip_norm': Value('float64'), 'betas': List(Value('float64')), 'eps': Value('float64')}, 'scheduler': {'type': Value('string'), 'num_warmup_steps': Value('int64'), 'name': Value('string')}, 'eval': {'n_episodes': Value('int64'), 'batch_size': Value('int64'), 'use_async_envs': Value('bool')}, 'wandb': {'enable': Value('bool'), 'disable_artifact': Value('bool'), 'project': Value('string'), 'entity': Value('null'), 'notes': Value('null'), 'run_id': Value('null'), 'mode': Value('null')}, 'use_rabc': Value('bool'), 'rabc_progress_path': Value('null'), 'rabc_kappa': Value('float64'), 'rabc_epsilon': Value('float64'), 'rabc_head_mode': Value('string'), 'rename_map': {}, 'checkpoint_path': Value('null')}
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 483, 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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
codebase_version: string
robot_type: string
total_episodes: int64
total_frames: int64
total_tasks: int64
chunks_size: int64
data_files_size_in_mb: int64
video_files_size_in_mb: int64
fps: int64
splits: struct<train: string>
child 0, train: string
data_path: string
video_path: string
features: struct<observation.state: struct<dtype: string, shape: list<item: int64>, names: list<item: string>> (... 1032 chars omitted)
child 0, observation.state: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 1, action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 2, observation.images.front: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.height (... 157 chars omitted)
child 0, dtype: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, names: list<item: string>
child 0, item: string
child 3, info: struct<video.height: int64, video.width: int64, video.codec: string, video.pix_fmt: string, video.is (... 75 chars omitted)
child 0, video.height: int64
chil
...
: null
child 11, pretrained_path: null
child 12, horizon: int64
child 13, n_action_steps: int64
child 14, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>
child 0, VISUAL: string
child 1, STATE: string
child 2, ACTION: string
child 15, drop_n_last_frames: int64
child 16, vision_backbone: string
child 17, crop_shape: list<item: int64>
child 0, item: int64
child 18, crop_is_random: bool
child 19, pretrained_backbone_weights: null
child 20, use_group_norm: bool
child 21, spatial_softmax_num_keypoints: int64
child 22, use_separate_rgb_encoder_per_camera: bool
child 23, down_dims: list<item: int64>
child 0, item: int64
child 24, kernel_size: int64
child 25, n_groups: int64
child 26, diffusion_step_embed_dim: int64
child 27, use_film_scale_modulation: bool
child 28, noise_scheduler_type: string
child 29, num_train_timesteps: int64
child 30, beta_schedule: string
child 31, beta_start: double
child 32, beta_end: double
child 33, prediction_type: string
child 34, clip_sample: bool
child 35, clip_sample_range: double
child 36, num_inference_steps: int64
child 37, do_mask_loss_for_padding: bool
child 38, optimizer_lr: double
child 39, optimizer_betas: list<item: double>
child 0, item: double
child 40, optimizer_eps: double
child 41, optimizer_weight_decay: double
child 42, scheduler_name: string
child 43, scheduler_warmup_steps: int64
seed: int64
steps: int64
to
{'dataset': {'repo_id': Value('string'), 'root': Value('string'), 'episodes': Value('null'), 'image_transforms': {'enable': Value('bool'), 'max_num_transforms': Value('int64'), 'random_order': Value('bool'), 'tfs': {'brightness': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'brightness': List(Value('float64'))}}, 'contrast': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'contrast': List(Value('float64'))}}, 'saturation': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'saturation': List(Value('float64'))}}, 'hue': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'hue': List(Value('float64'))}}, 'sharpness': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'sharpness': List(Value('float64'))}}, 'affine': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'degrees': List(Value('float64')), 'translate': List(Value('float64'))}}, 'blur': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'kernel_size': Value('int64'), 'sigma': List(Value('float64'))}}, 'erasing': {'weight': Value('float64'), 'type': Value('string'), 'kwargs': {'p': Value('float64'), 'scale': List(Value('float64')), 'ratio': List(Value('float64'))}}}}, 'revision': Value('null'), 'use_imagenet_stats': Value('bool'), 'video_backend': Value('string'), 'streaming': Value('bool')}, 'env': Value('null'), 'policy': {'type': Value('string'), 'n_obs_steps': Value('int64'), 'input_features': {'observation.imag
...
eps': Value('int64'), 'do_mask_loss_for_padding': Value('bool'), 'optimizer_lr': Value('float64'), 'optimizer_betas': List(Value('float64')), 'optimizer_eps': Value('float64'), 'optimizer_weight_decay': Value('float64'), 'scheduler_name': Value('string'), 'scheduler_warmup_steps': Value('int64')}, 'output_dir': Value('string'), 'job_name': Value('string'), 'resume': Value('bool'), 'seed': Value('int64'), 'num_workers': Value('int64'), 'batch_size': Value('int64'), 'steps': Value('int64'), 'eval_freq': Value('int64'), 'log_freq': Value('int64'), 'tolerance_s': Value('float64'), 'save_checkpoint': Value('bool'), 'save_freq': Value('int64'), 'use_policy_training_preset': Value('bool'), 'optimizer': {'type': Value('string'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'grad_clip_norm': Value('float64'), 'betas': List(Value('float64')), 'eps': Value('float64')}, 'scheduler': {'type': Value('string'), 'num_warmup_steps': Value('int64'), 'name': Value('string')}, 'eval': {'n_episodes': Value('int64'), 'batch_size': Value('int64'), 'use_async_envs': Value('bool')}, 'wandb': {'enable': Value('bool'), 'disable_artifact': Value('bool'), 'project': Value('string'), 'entity': Value('null'), 'notes': Value('null'), 'run_id': Value('null'), 'mode': Value('null')}, 'use_rabc': Value('bool'), 'rabc_progress_path': Value('null'), 'rabc_kappa': Value('float64'), 'rabc_epsilon': Value('float64'), 'rabc_head_mode': Value('string'), 'rename_map': {}, 'checkpoint_path': Value('null')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
lerobot-data-stir_bar (archived source)
This source repo mirrors the original mixed-contents stir_bar project
(dataset + checkpoints + eval videos) and is kept only for reproducibility.
Everything is preserved in the old/ subfolder.
Where to find things now
| What you want | Where to look |
|---|---|
| Training dataset | WetLabRoboData/lerobot-data-stir_bar_ds5 |
| Scratch diffusion policy | WetLabRoboData/diffusion-stir_bar-scratch |
| Eval rollouts | WetLabRoboData/eval-diffusion-stir_bar-scratch |
| Task collection | Stir Bar collection |
The original mirror lives at old/ within this repo.
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