The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: IndexError
Message: tuple index out of range
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2083, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 544, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 80, in _generate_tables
num_rows = _check_dataset_lengths(h5, self.info.features)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 358, in _check_dataset_lengths
if dset.shape[0] != num_rows:
~~~~~~~~~~^^^
IndexError: tuple index out of rangeNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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
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