The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators
inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 6321, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowTypeError: Unable to merge: Field json has incompatible types: struct<attributes: struct<>, chunk_grid: struct<configuration: struct<chunk_shape: list<item: int64>>, name: string>, chunk_key_encoding: struct<configuration: struct<separator: string>, name: string>, codecs: list<item: struct<configuration: struct<checksum: bool, level: int64>, name: string>>, data_type: string, fill_value: string, node_type: string, shape: list<item: int64>, storage_transformers: list<item: null>, zarr_format: int64> vs struct<attributes: struct<>, chunk_grid: struct<configuration: struct<chunk_shape: list<item: int64>>, name: string>, chunk_key_encoding: struct<configuration: struct<separator: string>, name: string>, codecs: list<item: struct<configuration: struct<checksum: bool, level: int64>, name: string>>, data_type: string, fill_value: bool, node_type: string, shape: list<item: int64>, storage_transformers: list<item: null>, zarr_format: int64>: Unable to merge: Field fill_value has incompatible types: string vs bool
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/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
mimicdroid-gr1
GR1 one-shot post-training set: 579 zarr episodes (4.7 GB unpacked) plus the retrieval manifest they
are matched with. Two embodiments share the same kitchen tasks — a floating-hand teleop demonstrator
(mimicdroid) and the GR1 humanoid (gr1) — so a policy can be conditioned on a demo from one and act
with the other.
| count | |
|---|---|
| episodes | 579 (gr1 253, mimicdroid 326) |
| windows | 93,371 |
| queries | 61,686, top-6 matches each |
| tasks | TurnOnFaucet 50, CloseLeftCabinetDoor 50, PnPSinkToCabinet 81, PnPSinkToRightCounterPlate 72, dth floating-hand pool 314, taskdemo 12 |
Layout
data/<episode>.tar # one tar per zarr episode (uncompressed; zarr chunks are already compressed)
manifest/ # retrieval manifest + normalization statistics
windows.npz # ep / w0 / w1 / emb / cls / keep for every window
pairs.npz # query_idx, match_idx (N, 6), match_dist
state57_stats*.json # state normalization (percentile-hybrid variant used by the v6 recipe)
action57_stats.json, abs_action57_stats_pctl.json, cmd_delta57_stats.json
t5_embeddings.pkl # per-episode T5 text embeddings
episodes.txt # the 579 episode names, in manifest order
stats.json # the table above, machine-readable
Each episode unpacks to a zarr group:
<episode>/
images.front_1 images.center_top images.sideleft images.sideright # 4 camera views
state57 action57 abs_action57 # 57-d state / action / absolute command
meta
Use
mkdir -p egoverse && for t in data/*.tar; do tar xf "$t" -C egoverse; done
Point the training config at the unpacked root and the manifest directory:
HUM_ROOT = <...>/egoverse # dataset class resolves manifest `ep` names under this root
manifest_dir = <...>/manifest
windows.npz:ep is exactly <episode> in data/<episode>.tar, so the two halves line up with no
renaming. Absolute-command targets (abs_action57) are what the v6 post-train regresses; executing the
predictions reproduces the demo tracking dynamics.
Provenance
Episodes were generated in RoboCasa kitchens: a floating-hand pool per task and level, retargeted to
GR1, then selected into the final per-task sets. The selection provenance is kept per demo
(v6_origin) in the source HDF5s, which are not part of this upload — this repository is the
training-ready half (manifest + zarr).
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