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/hdf5/hdf5.py", line 49, in _split_generators
import h5py
ModuleNotFoundError: No module named 'h5py'
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 71, 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.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
DemystifyActionSpace Dataset
Overview
This dataset is collected for the paper
"Demystifying Action Space Design for Robotic Manipulation Policies".
The dataset contains teleoperated demonstrations for a set of tabletop manipulation tasks using AgileX robotic platforms. It is designed to support empirical studies on how different action space designs affect policy learning for robotic manipulation.
The dataset includes both single arm and bimanual tasks, as well as a small cross embodiment subset collected on a different robot platform.
Tasks
touch — Touch Cube
The robot reaches toward a cube and makes contact with it.
This task evaluates basic reaching and contact behavior.
pick_cup — Pick Up Cup
The robot grasps a cup and lifts it from the table.
This task focuses on grasp acquisition and lifting.
pick_place — Pick and Place Cup
The robot picks up a cup and places it at a target location.
This task evaluates sequential manipulation involving grasping, transport, and placement.
bowl — Bimanual Cube Transfer
The robot uses two arms to transfer a cube from one gripper to the other.
This task evaluates bimanual coordination and object handoff.
Cross Embodiment Dataset
To study cross embodiment generalization, we provide a small subset collected on a different robot platform.
airbot(touch) — Touch Cube on AIRBOT
The same Touch Cube task performed using the AIRBOT platform.
The subset of touch and airbot(touch) can be used to evaluate policy transfer across robot embodiments.
Data Format
Each trajectory contains:
- observations
- RGB images
- robot states
- actions
The dataset is stored in a trajectory based format compatible with robot learning pipelines.
Intended Use
This dataset is intended for research on:
- imitation learning for robotic manipulation
- action space design in robot policies
- policy learning with teleoperated demonstrations
- cross embodiment policy generalization
- Downloads last month
- 235