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license: mit
pretty_name: GraspTwin
task_categories:
- robotics
tags:
- grasping
- manipulation
- language-conditioned
- 3d
- meshes
- digital-twin
size_categories:
- n<1K
---
# GraspTwin benchmark scenes
Digital twins of real tabletop scenes for language-conditioned grasping: one
reconstructed `.glb` mesh per object, the object poses needed to rebuild each scene in
simulation, and the natural-language tasks with their target objects.
- 9 scenes, 5 subscenes each (45 subscenes total)
- 8 household objects per subscene (`scene_8_4` has 10)
- 10 natural-language tasks per scene, each tied to a target object
- about 2.6 GB of meshes
## Layout
```
scene_N/
tasks.json # 10 task strings, e.g. "grab the mug by the handle"
scene_N_M/ # subscene M = 1..5 (same objects, different arrangement)
objects.json # names of the objects in the subscene
target_objects.json # target object per task, index-aligned with ../tasks.json
boxes.json # 2D box [x1, y1, x2, y2] in pixels of the 1920x1080
# scene capture, per object name
meshes/
<object name>.glb # one mesh per object (trimesh export)
transforms.json # per-object translation, rotation_wxyz, scale
settle_transforms.json # same, after the objects settle under physics in sim
```
Object names are the keys used across `objects.json`, `boxes.json`, `transforms.json`
and the mesh filenames, so a task can be resolved to its mesh by name.
## Loading a subscene
```python
import json, trimesh
from pathlib import Path
sub = Path("scene_1/scene_1_1")
tasks = json.loads((sub.parent / "tasks.json").read_text())
targets = json.loads((sub / "target_objects.json").read_text())
poses = json.loads((sub / "meshes" / "transforms.json").read_text())["objects"]
for task, obj in zip(tasks, targets):
mesh = trimesh.load(sub / "meshes" / f"{obj}.glb")
print(task, "->", obj, poses[obj]["translation"])
```
## License
MIT.
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