| --- |
| license: cc-by-4.0 |
| task_categories: [robotics] |
| tags: [robotics, manipulation, grasping, grasp-annotation, franka, maniguard] |
| pretty_name: ManiGuard Grasp Annotations |
| size_categories: [1K<n<10K] |
| --- |
| |
| # 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`](https://huggingface.co/datasets/IDEAS-Lab-Northwestern/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](https://github.com/NU-IDEAS-Lab/ManiGuard)); |
| 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](https://github.com/StanfordVL/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: |
|
|
| ```bash |
| hf download IDEAS-Lab-Northwestern/maniguard-grasp-annotations \ |
| --repo-type dataset --local-dir outputs/grasp_annotation |
| ``` |
|
|
| See the [data-collection docs](https://nu-ideas-lab.github.io/ManiGuard/data_collection/) |
| for the full scripted-datagen pipeline. |
|
|
| ## Paper & Citation |
|
|
| Part of **ManiGuard** — [paper (arXiv:2608.17386)](https://arxiv.org/abs/2608.17386) · |
| [code](https://github.com/NU-IDEAS-Lab/ManiGuard) · |
| [docs](https://nu-ideas-lab.github.io/ManiGuard/) |
|
|
| ```bibtex |
| @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}, |
| } |
| ``` |
|
|
