ManiGuard: Benchmark & Datasets
Collection
ManiGuard-Bench tasks, the long-finger Franka asset, and the 6-family SFT demo trajectories (LeRobot v2.1). • 9 items • Updated • 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: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ManiGuard Grasp Annotations
The curated grasp database behind ManiGuard's automated demonstration generation:
1,547 annotated 6-DoF grasps over 221 object instances across the six
ManiGuard-Bench task families. Each grasp is an eef-link target pose in the object's
local frame for the long-finger Franka Panda gripper
(franka-panda-longfinger);
at runtime the world grasp target is simply T_eef_world = T_object_world @ T_grasp_local.
Grasps were placed and reviewed per object instance in a viser-based annotation tool
(maniguard/data/datagen/annotation/ in the codebase);
every object carries several alternatives (median 5, up to 40) so the motion planner can
fall back when a grasp is unreachable or blocked.
Files
| File | Contents |
|---|---|
grasp_annotations.json |
the database: objects keyed by category/model, each with bbox_size, upright_orientation_xyzw, and a grasps list (position, orientation_xyzw in the object-local frame, approach_hint, source) |
cabinet_geom.json |
cached drawer-link geometry scalars for the cabinet family (slide axis/sign, stroke, root-local AABBs) |
source per grasp: freedrag (953) and click (582) — the tool's two manual placement
modes — plus topdown_gen (12) tool-proposed top-down poses.
No upstream geometry
This dataset contains poses and metadata only — no meshes or other geometry derived
from the BEHAVIOR-1K assets, per the
upstream asset terms. The mesh fields are relative path strings used by the annotation
tool. Running ManiGuard's datagen does not require meshes; to use the annotation
tool (add or edit grasps), regenerate them locally from your own BEHAVIOR-1K download
with maniguard/data/datagen/annotation/extract_meshes.py.
Usage
ManiGuard's datagen loader (maniguard.data.datagen.grasp_db) reads the database from
outputs/grasp_annotation/ under the repo root:
hf download IDEAS-Lab-Northwestern/maniguard-grasp-annotations \
--repo-type dataset --local-dir outputs/grasp_annotation
See the data-collection docs for the full scripted-datagen pipeline.
Paper & Citation
Part of ManiGuard — paper (arXiv:2608.17386) · code · docs
@misc{peng2026maniguard,
title = {{MANIGUARD}: A Benchmark and Data Suite for Specification-Grounded
Safety Evaluation and Improvement of Robotic Manipulation},
author = {Peng, Yiyan and Wang, Philip and Zhan, Simon Sinong and Lyu, Yiqi
and Ni, Zhenyang and Yan, Jixin and Wong, Fiorelli and Jiao, Ruochen
and Yin, Hang and Cao, Xinyu and Shao, Huajie and Li, Manling
and Zhang, Ruohan and Zhu, Qi},
year = {2026},
eprint = {2608.17386},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2608.17386},
}
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