spatialgym/SpatialGym_Figural

SpatialGym Figural Objects

Object packs for the figural (tabletop) tasks of SpatialGym — Pose Matching, Mental Rotation and Active Closure. Each pack is a set of self-contained USD objects baked from one curated catalog of source meshes (RoboTwin 2.0 rigid objects + YCB google_16k scans), plus a manifest.json the environments read. The other six figural packs (polycubes, CLEVR objects, spatial-relation solids, assembly, net folding) are generated locally by assets/download.sh and are not part of this repository.

Overview

Pack	Objects	Sources	What the environment gets	Download	Unpacked
pose_matching	411	RoboTwin 354 · YCB 57	rest pose, metric size, PBR textures	1.4 GB	1.4 GB
mental_rotation	151	RoboTwin 132 · YCB 19	upright, longest axis 1.7 m, yaw-asymmetric subset	534 MB	527 MB
active_closure	307	RoboTwin + YCB	voxelised body (cube PointInstancer) + 4-way options	4 MB	20 MB

Files

SpatialGym_Figural/
├── README.md
├── pose_matching.tar.gz      # unpacks to pose_matching/{manifest.json, objects/<id>.usd, objects/textures/}
├── mental_rotation.tar.gz    # unpacks to mental_rotation/{manifest.json, objects/<id>.usd, objects/textures/}
└── active_closure.tar.gz     # unpacks to active_closure/{manifest.json, <id>.usd}

SpatialGym_IsaacLab/assets/download.sh figural in the SpatialGym repository fetches the three packs into assets/data/figural/, builds the six generated packs next to them and checks every pack against the version its code expects.

Packs

pose_matching — objects/<id>.usd, one per catalog entry, in its canonical rest pose at real-world size; textures externalised under objects/textures/.

_{Pose Matching: one task frame per scene, difficulty 1–3.}

Key	Meaning
id, usd	Object id and USD path relative to the pack
label_l1, label_l2	Coarse / fine object labels shown to the agent
footprint_cm, height_cm	Rest-pose footprint [w, l] and height
yaw_symmetric	true ⇒ the object looks the same at every yaw and is excluded from the manipulable pool
source, orig_id, variant	Provenance: source dataset, original id, texture / shape variant

mental_rotation — the yaw-asymmetric subset of the same catalog, re-baked upright (rest rotation zeroed) and rescaled so every object's longest axis is 1.7 m. No per-object difficulty: object complexity is not an axis of this task.

_{Mental Rotation: top row the locally generated polycube pool (not in this repository), bottom row this pack's curated objects.}

Key	Meaning
id, usd, label_l1, label_l2, source, orig_id, variant	As above
dims_m	Bounding-box extent [x, y, z] in metres, measured off the baked USD

active_closure — one <id>.usd per object holding a PointInstancer of cubes (the voxelised body, 348–30 221 voxels), floated in a booth for the closure question.

_{Active Closure: one object per column, visibility rungs 1–3 down the column.}

Key	Meaning
id, usd, category	Object id, USD path, category label
num_voxels, size_m	Voxel count and longest-axis size of the voxelised body
options, answer_idx	A 4-way option set and the correct index. The environment draws its own option set at load; these are kept for older code

All figures are frames the tasks themselves render (oracle runs, the environments' own camera and lighting).

Sources and licensing

The objects are derived from two source datasets:

• RoboTwin 2.0 object library — code and dataset released under the MIT license. Chen et al., RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation, arXiv:2506.18088, 2025.
• YCB Object and Model Set (google_16k scans) — Calli et al., The YCB Object and Model Set: Towards Common Benchmarks for Manipulation Research, ICAR 2015. Model files are distributed under CC BY 4.0.

The baked USD packs in this repository are provided for research use; please cite SpatialGym and the source datasets above when you use them.