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/folder_based_builder/folder_based_builder.py", line 251, in _split_generators
raise ValueError(
"`file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files"
)
ValueError: `file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files
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.
HiveBoard ManiSkill demonstrations
Successful scripted demonstrations for 13 HiveBoard manipulation tasks in ManiSkill with a Franka FR3 robot. There are 150 unique episodes per task: 120 with a fixed module pose and 30 with varied module placement. Object placement is randomized. The varied set also randomizes the robot's initial pose. The ball valve and circuit breaker modules keep their orientation fixed; the ball valve ring uses the same fixed orientation as in the 120-episode set.
The dataset contains 1,950 unique successful episodes. The train, validation, and test splits contain 1,560, 195, and 195 episodes respectively. Splits are assigned by episode seed and do not duplicate an episode across splits. Training pipelines may repeat episodes for sampling; those repeats are not included here.
Files
metadata.jsonl has one record per episode with its task, variant, split, seed, language prompt, frame count, and relative paths to the HDF5 episode and two MP4 videos. Files are stored under data/<task>/<variant>/. The HDF5 files use the Oopsie-style 10 Hz episode format. They contain robot observations and Cartesian actions. Each episode has front and wrist camera videos referenced by name from the HDF5 file. summary.json contains counts by task, variant, and split.
Example:
import json
from pathlib import Path
rows = [json.loads(line) for line in Path("metadata.jsonl").open()]
train = [row for row in rows if row["split"] == "train"]
episode_path = Path(train[0]["episode_file"])
front_video_path = Path(train[0]["front_video"])
Download the repository first so the relative paths resolve. The dataset is model independent: use the metadata to select episodes and convert the HDF5 and videos to the representation required by your trainer.
Scope
These are simulated, success-only demonstrations. They do not contain failed attempts or real robot observations. The source 20 Hz ManiSkill recordings and derived LeRobot training sets are not part of this release. The LeRobot sets used for a particular experiment may oversample some episodes; this repository keeps each source episode once.
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