Learning Visual Feature-Based World Models via Residual Latent Action
Paper • 2605.07079 • Published • 2
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<30: list<item: struct<version: int64, dataset_idx: int64, group_name: string, traj_id: string, frame_id: int64, sampled_horizon: int64, feasible_horizon: int64, camera_keys: list<item: string>, rgb_variant: string, interaction_frame_indices: null, dataset_root: string>>>
to
{'60': List({'version': Value('int64'), 'dataset_idx': Value('int64'), 'group_name': Value('string'), 'traj_id': Value('string'), 'frame_id': Value('int64'), 'sampled_horizon': Value('int64'), 'feasible_horizon': Value('int64'), 'camera_keys': List(Value('string')), 'rgb_variant': Value('string'), 'interaction_frame_indices': Value('null'), 'dataset_root': Value('string')})}
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 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, 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 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, 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/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2255, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1804, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2101, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<30: list<item: struct<version: int64, dataset_idx: int64, group_name: string, traj_id: string, frame_id: int64, sampled_horizon: int64, feasible_horizon: int64, camera_keys: list<item: string>, rgb_variant: string, interaction_frame_indices: null, dataset_root: string>>>
to
{'60': List({'version': Value('int64'), 'dataset_idx': Value('int64'), 'group_name': Value('string'), 'traj_id': Value('string'), 'frame_id': Value('int64'), 'sampled_horizon': Value('int64'), 'feasible_horizon': Value('int64'), 'camera_keys': List(Value('string')), 'rgb_variant': Value('string'), 'interaction_frame_indices': Value('null'), 'dataset_root': Value('string')})}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.
Learning Visual Feature-Based World Models via Residual Latent Action
This repository contains the dataset artifacts for the paper Learning Visual Feature-Based World Models via Residual Latent Action.
Dataset Description
The primary dataset included here is Maniskill3DWorld, which consists of multi-modal ManiSkill trajectories. It is designed for 3D and multi-view research and includes:
- 7-camera RGB, depth, and masks
- Animated robot meshes
- Voxel point clouds
Sample Usage
You can download and extract the ManiSkill dataset using the Hugging Face CLI:
# Create data directory
mkdir -p data && cd data
# Download the dataset
hf download xyzhang368/RLA-WM --repo-type dataset --include "maniskill.tar" --local-dir .
# Extract the data
tar -xf maniskill.tar
Citation
@article{zhang2026learning,
title={{Learning Visual Feature-Based World Models via Residual Latent Action}},
author={Zhang, Xinyu and Xu, Zhengtong and Tao, Yutian and Wang, Yeping and She, Yu and Boularias, Abdeslam},
journal={arXiv preprint arXiv:2605.07079},
year={2026},
eprint={2605.07079},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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