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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
seed: int64
success: bool
chunks: int64
env_steps: int64
seconds: double
initial_hashes: struct<state.base_position: string, state.base_rotation: string, state.end_effector_position_relativ (... 193 chars omitted)
child 0, state.base_position: string
child 1, state.base_rotation: string
child 2, state.end_effector_position_relative: string
child 3, state.end_effector_rotation_relative: string
child 4, state.gripper_qpos: string
child 5, video.robot0_agentview_left: string
child 6, video.robot0_agentview_right: string
child 7, video.robot0_eye_in_hand: string
ttt_position: int64
prediction_frames: list<item: int64>
child 0, item: int64
intermediate_frames: list<item: int64>
child 0, item: int64
action_hashes: list<item: struct<action.end_effector_position: string, action.end_effector_rotation: string, action (... 81 chars omitted)
child 0, item: struct<action.end_effector_position: string, action.end_effector_rotation: string, action.gripper_cl (... 69 chars omitted)
child 0, action.end_effector_position: string
child 1, action.end_effector_rotation: string
child 2, action.gripper_close: string
child 3, action.base_motion: string
child 4, action.control_mode: string
observation_hashes: list<item: struct<state.base_position: string, state.base_rotation: string, state.end_effector_posit (... 205 chars omitted)
child 0, item: struct<state.base_position: string, state.base_rotation: string, state.end_effector_position_relativ (
...
ld 0, item: int64
child 3, subset_seed: int64
child 4, train_episode_count: int64
child 5, train_frames: int64
child 6, parquet_schema: string
child 7, test_record_count: int64
child 8, matched_test_records: int64
child 9, tests: list<item: struct<file: string, seed: int64, matches: list<item: null>>>
child 0, item: struct<file: string, seed: int64, matches: list<item: null>>
child 0, file: string
child 1, seed: int64
child 2, matches: list<item: null>
child 0, item: null
child 10, metadata_files: list<item: string>
child 0, item: string
NavigateKitchen: struct<train_path: string, available_episodes: int64, selected_episode_ids: list<item: int64>, subse (... 259 chars omitted)
child 0, train_path: string
child 1, available_episodes: int64
child 2, selected_episode_ids: list<item: int64>
child 0, item: int64
child 3, subset_seed: int64
child 4, train_episode_count: int64
child 5, train_frames: int64
child 6, parquet_schema: string
child 7, test_record_count: int64
child 8, matched_test_records: int64
child 9, tests: list<item: struct<file: string, seed: int64, matches: list<item: null>>>
child 0, item: struct<file: string, seed: int64, matches: list<item: null>>
child 0, file: string
child 1, seed: int64
child 2, matches: list<item: null>
child 0, item: null
child 10, metadata_files: list<item: string>
child 0, item: string
to
{'TurnOnMicrowave': {'train_path': Value('string'), 'available_episodes': Value('int64'), 'selected_episode_ids': List(Value('int64')), 'subset_seed': Value('int64'), 'train_episode_count': Value('int64'), 'train_frames': Value('int64'), 'parquet_schema': Value('string'), 'test_record_count': Value('int64'), 'matched_test_records': Value('int64'), 'tests': List({'file': Value('string'), 'seed': Value('int64'), 'matches': List(Value('null'))}), 'metadata_files': List(Value('string'))}, 'TurnOnElectricKettle': {'train_path': Value('string'), 'available_episodes': Value('int64'), 'selected_episode_ids': List(Value('int64')), 'subset_seed': Value('int64'), 'train_episode_count': Value('int64'), 'train_frames': Value('int64'), 'parquet_schema': Value('string'), 'test_record_count': Value('int64'), 'matched_test_records': Value('int64'), 'tests': List({'file': Value('string'), 'seed': Value('int64'), 'matches': List(Value('null'))}), 'metadata_files': List(Value('string'))}, 'NavigateKitchen': {'train_path': Value('string'), 'available_episodes': Value('int64'), 'selected_episode_ids': List(Value('int64')), 'subset_seed': Value('int64'), 'train_episode_count': Value('int64'), 'train_frames': Value('int64'), 'parquet_schema': Value('string'), 'test_record_count': Value('int64'), 'matched_test_records': Value('int64'), 'tests': List({'file': Value('string'), 'seed': Value('int64'), 'matches': List(Value('null'))}), 'metadata_files': List(Value('string'))}}
because column names don't match
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/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
seed: int64
success: bool
chunks: int64
env_steps: int64
seconds: double
initial_hashes: struct<state.base_position: string, state.base_rotation: string, state.end_effector_position_relativ (... 193 chars omitted)
child 0, state.base_position: string
child 1, state.base_rotation: string
child 2, state.end_effector_position_relative: string
child 3, state.end_effector_rotation_relative: string
child 4, state.gripper_qpos: string
child 5, video.robot0_agentview_left: string
child 6, video.robot0_agentview_right: string
child 7, video.robot0_eye_in_hand: string
ttt_position: int64
prediction_frames: list<item: int64>
child 0, item: int64
intermediate_frames: list<item: int64>
child 0, item: int64
action_hashes: list<item: struct<action.end_effector_position: string, action.end_effector_rotation: string, action (... 81 chars omitted)
child 0, item: struct<action.end_effector_position: string, action.end_effector_rotation: string, action.gripper_cl (... 69 chars omitted)
child 0, action.end_effector_position: string
child 1, action.end_effector_rotation: string
child 2, action.gripper_close: string
child 3, action.base_motion: string
child 4, action.control_mode: string
observation_hashes: list<item: struct<state.base_position: string, state.base_rotation: string, state.end_effector_posit (... 205 chars omitted)
child 0, item: struct<state.base_position: string, state.base_rotation: string, state.end_effector_position_relativ (
...
ld 0, item: int64
child 3, subset_seed: int64
child 4, train_episode_count: int64
child 5, train_frames: int64
child 6, parquet_schema: string
child 7, test_record_count: int64
child 8, matched_test_records: int64
child 9, tests: list<item: struct<file: string, seed: int64, matches: list<item: null>>>
child 0, item: struct<file: string, seed: int64, matches: list<item: null>>
child 0, file: string
child 1, seed: int64
child 2, matches: list<item: null>
child 0, item: null
child 10, metadata_files: list<item: string>
child 0, item: string
NavigateKitchen: struct<train_path: string, available_episodes: int64, selected_episode_ids: list<item: int64>, subse (... 259 chars omitted)
child 0, train_path: string
child 1, available_episodes: int64
child 2, selected_episode_ids: list<item: int64>
child 0, item: int64
child 3, subset_seed: int64
child 4, train_episode_count: int64
child 5, train_frames: int64
child 6, parquet_schema: string
child 7, test_record_count: int64
child 8, matched_test_records: int64
child 9, tests: list<item: struct<file: string, seed: int64, matches: list<item: null>>>
child 0, item: struct<file: string, seed: int64, matches: list<item: null>>
child 0, file: string
child 1, seed: int64
child 2, matches: list<item: null>
child 0, item: null
child 10, metadata_files: list<item: string>
child 0, item: string
to
{'TurnOnMicrowave': {'train_path': Value('string'), 'available_episodes': Value('int64'), 'selected_episode_ids': List(Value('int64')), 'subset_seed': Value('int64'), 'train_episode_count': Value('int64'), 'train_frames': Value('int64'), 'parquet_schema': Value('string'), 'test_record_count': Value('int64'), 'matched_test_records': Value('int64'), 'tests': List({'file': Value('string'), 'seed': Value('int64'), 'matches': List(Value('null'))}), 'metadata_files': List(Value('string'))}, 'TurnOnElectricKettle': {'train_path': Value('string'), 'available_episodes': Value('int64'), 'selected_episode_ids': List(Value('int64')), 'subset_seed': Value('int64'), 'train_episode_count': Value('int64'), 'train_frames': Value('int64'), 'parquet_schema': Value('string'), 'test_record_count': Value('int64'), 'matched_test_records': Value('int64'), 'tests': List({'file': Value('string'), 'seed': Value('int64'), 'matches': List(Value('null'))}), 'metadata_files': List(Value('string'))}, 'NavigateKitchen': {'train_path': Value('string'), 'available_episodes': Value('int64'), 'selected_episode_ids': List(Value('int64')), 'subset_seed': Value('int64'), 'train_episode_count': Value('int64'), 'train_frames': Value('int64'), 'parquet_schema': Value('string'), 'test_record_count': Value('int64'), 'matched_test_records': Value('int64'), 'tests': List({'file': Value('string'), 'seed': Value('int64'), 'matches': List(Value('null'))}), 'metadata_files': List(Value('string'))}}
because column names don't match
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 1879, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
TurnOnMicrowave dict | TurnOnElectricKettle dict | NavigateKitchen dict |
|---|---|---|
{
"train_path": "/data/changyi/wam-b300/n15/data/v1.0/pretrain/atomic/TurnOnMicrowave/20250819/lerobot",
"available_episodes": 107,
"selected_episode_ids": [
0,
1,
2,
3,
4,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
2... | {
"train_path": "/data/changyi/wam-b300/n15/data/v1.0/pretrain/atomic/TurnOnElectricKettle/20250820/lerobot",
"available_episodes": 108,
"selected_episode_ids": [
0,
1,
2,
3,
4,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
... | {
"train_path": "/data/changyi/wam-b300/n15/data/v1.0/pretrain/atomic/NavigateKitchen/20250821/lerobot",
"available_episodes": 503,
"selected_episode_ids": [
1,
15,
17,
22,
24,
26,
27,
28,
44,
56,
58,
62,
64,
67,
68,
84,
98,
100,
103,
... |
Experimental results archive
Transfer complete: 85 archives and the results-shard-index.json are available; all archive byte sizes were verified on 2026-09-21. Archives are uploaded sequentially and verified for remote byte size. The final results-shard-index.json will map original relative paths to tar files after the complete transfer. Missing index means the transfer is not yet declared complete.
Model checkpoints: https://huggingface.co/yyoraa/robocasa-ttt-checkpoints Code, detailed caveats and TODO: https://github.com/yyoraa/ttt_memory/tree/main/experiments/2026-09-20-handoff (private repository).
Includes experimental outputs, videos, arrays, seeds/initial-state records where generated, and logs. Historical experiments include smoke tests, failures, incomplete searches and unmatched controls: a file's presence is not evidence of a successful or completed experiment. Consult the individual metadata and GitHub handoff explanations.
Paths inside archives start with workspace-results, workspace-logs, n15-eval, n15-ttt, mm-results or mm-logs. They are evidence exports, not training datasets. Source dataset and upstream model terms continue to apply.
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