--- 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/ .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.