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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
environment: string
episodes: int64
eval_states: string
eval_states_sha256: string
policy: string
results: list<item: struct<episode: int64, reward: double, seed: int64, steps: int64, success: bool>>
child 0, item: struct<episode: int64, reward: double, seed: int64, steps: int64, success: bool>
child 0, episode: int64
child 1, reward: double
child 2, seed: int64
child 3, steps: int64
child 4, success: bool
reuse_observations: bool
seed_per_episode: bool
success_rate: double
successes: int64
state_shape: list<item: int64>
child 0, item: int64
dataset: string
env_name: string
sha256: string
seed_start: int64
to
{'dataset': Value('string'), 'env_name': Value('string'), 'episodes': Value('int64'), 'seed_start': Value('int64'), 'sha256': Value('string'), 'state_shape': List(Value('int64'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
environment: string
episodes: int64
eval_states: string
eval_states_sha256: string
policy: string
results: list<item: struct<episode: int64, reward: double, seed: int64, steps: int64, success: bool>>
child 0, item: struct<episode: int64, reward: double, seed: int64, steps: int64, success: bool>
child 0, episode: int64
child 1, reward: double
child 2, seed: int64
child 3, steps: int64
child 4, success: bool
reuse_observations: bool
seed_per_episode: bool
success_rate: double
successes: int64
state_shape: list<item: int64>
child 0, item: int64
dataset: string
env_name: string
sha256: string
seed_start: int64
to
{'dataset': Value('string'), 'env_name': Value('string'), 'episodes': Value('int64'), 'seed_start': Value('int64'), 'sha256': Value('string'), 'state_shape': List(Value('int64'))}
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.
Single-task Transfer SAILOR and Retrieval Artifacts
Artifacts from the public code repository
RahulSChand/single-task-transfer.
Contents
artifacts/encoders/: broad and pilot SAILOR checkpoints, configs, frozen normalization statistics, and TensorBoard logs.artifacts/retrieval/: frozen embeddings, Behavior Retrieval scores, exact global-IWR KDE scores, released batch-local IWR reference scores, top-100 selections, manual-review videos, and plots.artifacts/policies/: integration-only policy smoke artifacts.manifests/: deterministic episode manifests, source file hashes, and dataset counts.results/: benchmark outputs, environment locks, repository SHAs, code checksums, and preservation inventories.logs/andvideos/: execution logs and simulator/retrieval review media.
Main run
- Encoder: seven-task broad SAILOR, 16-D latent, ten-step chunks.
- Training: batch 128, 90,000 updates, seed 1.
- Representation pool: 100 demonstrations each from Coffee Preparation, Stack, Stack Three, Pick Place, Square, Nut Assembly, and Three Piece Assembly D0.
- Retrieval pool: fixed 100 Coffee Preparation demonstrations only.
- Positive-control target: 10 deterministic seed-0 Coffee D0 demonstrations, used only as frozen-encoder queries.
Data policy
The original MimicGen HDF5 datasets and generated RLDS copies are intentionally
not redistributed here. Obtain them from the official MimicGen release or the
amandlek/mimicgen_datasets mirror and verify them using the included manifests.
The artifacts are research outputs and may inherit constraints from the source code, datasets, and simulator assets used to create them. Consult those upstream licenses before redistribution or commercial use.
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