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 250, 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.
Data For A Latent Action World Model: Egocentric Human Video And Panda Demonstrations
The data behind https://github.com/VinayMohan0409/humanoid-submission : a world model co-trained on phone video of a human hand and a few simulated robot demonstrations, then used to drive a Panda arm toward goal images in simulation.
human_videos/ (64 clips, collected by the author)
Egocentric phone clips, 832 × 464 at 60 fps, of one hand moving a single object on a desk: cards, a cube, a cup, a cylinder or a duster. 8 clips include a drop.
metadata.csv lists file_name, object and drop. No action labels.
panda_demos/ (30 episodes)
Scripted pick-and-place of a cube, cuboid or cylinder among 3 to 5 random distractors in robosuite 1.4.0, 20 Hz, agentview camera at 224 × 384.
21 clean placements, 3 released mid-carry, 3 edge grasps and 3 missed grasps (mode and outcome in metadata.csv), so dropped and failed grasps are included.
One HDF5 file per episode (episodes/ep_000.h5 … ep_029.h5, 80 to 252 steps) plus the same frames as videos/*.mp4:
| Dataset | Shape | Meaning |
|---|---|---|
rgb |
(T+1, 224, 384, 3) uint8 | agentview camera |
action |
(T, 7) float32 | OSC_POSE command: dx, dy, dz, drot x, y, z in [-1, 1] (0.05 m and 0.5 rad per unit), gripper (-1 open, 1 closed) |
eef_pos, eef_quat |
(T+1, 3), (T+1, 4) | end-effector pose |
gripper_qpos |
(T+1, 2) | finger joint positions |
object_pos, object_quat |
(T+1, 3), (T+1, 4) | target object pose |
distractor_pos |
(T+1, n, 3) | distractor positions |
Attributes: shape, mode, outcome, half (object half-extents, m), colour, target (table xy), n_distractors, attempts, final_dist_to_target.
Regenerate with python -m src.sim.scenes --out data/panda_demos --episodes 30 --seed 0 from the repository. The 200-episode play sets used for the inverse dynamics model are regenerated with python -m src.sim.play.
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