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README.md

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+---
+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},
+}
+```