FACT: Failure-Aware Causal Training for World-Action Models
Paper • 2608.10232 • Published
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
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 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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.
RoboTwin 2.0 Demonstrations (LeRobot v2.1)
Expert demonstrations for the 50 RoboTwin 2.0 benchmark tasks, generated with the RoboTwin simulator (MIT license) and packaged in LeRobot v2.1 format. Used to train FACT.
Paper: FACT: Failure-Aware Causal Training for World-Action Models
Project page: https://fact-wam.github.io
Code: https://github.com/Bariona/FACT
Contents
Single archive robotwin-v2.tar (84.5 GB, ~169 GB extracted).
- sha256:
f3392ca1dae75e7eab937094d8b9d118c622081d70eeed9655f9307b27b61f97
datasets/RoboTwin/
Clean/<task_name>/ # demo_clean scenes — 50 tasks × 50 episodes
Randomized/<task_name>/ # demo_randomized scenes — 50 tasks × 50 episodes
Each <task_name>/ is a LeRobot v2.1 dataset directory (meta/, data/, videos/).
Schema
- Robot: bimanual Aloha (
robot_type: aloha), 14-DOF state/action (6 joints + gripper per arm), 15 fps - Features:
observation.state,action,observation.images.cam_high(640×480),observation.images.cam_left_wrist,observation.images.cam_right_wrist, plus LeRobot index/timestamp fields - Language instruction per episode in
meta/
Usage
huggingface-cli download Bariona/robotwin-v2 robotwin-v2.tar \
--repo-type dataset --local-dir ./datasets
tar -xf ./datasets/robotwin-v2.tar -C ./datasets
Provenance & license
Trajectories were collected with the RoboTwin 2.0 expert data pipeline (RoboTwin-Platform/RoboTwin, MIT License, © 2025 Tianxing Chen) and converted to LeRobot v2.1. Redistributed under MIT in accordance with the upstream license. If you use this data, please cite RoboTwin.
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