| --- |
| license: cc-by-4.0 |
| task_categories: |
| - robotics |
| tags: |
| - cloth-simulation |
| - sim-to-real |
| - point-cloud |
| - benchmark |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # RGBench Cloth Sim-to-Real (v1) |
|
|
| 🌐 Project page: <https://rgbench.github.io/> · 📦 Code: <https://github.com/hwk0809/RGBench> |
|
|
| Nine carefully captured garments — three bimanual manipulation actions |
| each (fling / fold / grasp) — with **real-world ground truth point |
| clouds** for evaluating any cloth simulator's sim-to-real gap. Released |
| as the evaluation half of the AAAI 2026 paper *[Real Garment Benchmark |
| (RGBench)](https://rgbench.github.io/)*. |
|
|
| The larger 6 000+ garment-mesh asset library and the GarmentDynamics |
| simulator from the paper are on their way to open-sourcing in follow-up |
| releases. This dataset is the piece you need to **benchmark any cloth |
| simulator against real captured dynamics today**. |
|
|
| ## Contents |
|
|
| | Path | What's inside | |
| | --- | --- | |
| | `<garment>/<garment>_<action>_<ts>/calibration/` | Camera extrinsics, initial object pose | |
| | `<garment>/<garment>_<action>_<ts>/joints/` | Bimanual robot joint + end-effector CSVs | |
| | `<garment>/<garment>_<action>_<ts>/segment_pcds/` | Segmented cloth point clouds (cloth-only, world frame after extrinsics) | |
| | `meshes/<Garment>/*.obj`, `*.usda` | Cloth garment meshes used by the simulators | |
|
|
| 9 garments × {grasp, fold, fling} actions, captured with a Piper bimanual |
| gripper and a RealSense D455. ~100 evaluation samples in total. Two |
| garments (`grey_sunwear`, `khaki_blazer`) have non-manifold meshes and |
| are **not** part of the paper's published baselines — see the |
| [results/](https://github.com/hwk0809/RGBench/tree/main/results) folder |
| in the GitHub repo for the published baseline numbers and the |
| methodology note. |
|
|
| Garment meshes ship in four resolutions for `green_tshirt` (5k / 10k / |
| 20k / 40k triangles) under `meshes/Green_Tshirt_Compare/` so researchers |
| can study how cloth mesh resolution affects sim-to-real fidelity. |
|
|
| ## Quickstart |
|
|
| ```bash |
| git clone https://github.com/hwk0809/RGBench |
| cd RGBench |
| bash setup.sh # installs deps + downloads this dataset |
| make benchmark sim=pybullet # runs the smoke-test sample |
| ``` |
|
|
| If you only want to fetch the data: |
|
|
| ```bash |
| pip install huggingface_hub |
| python -m huggingface_hub.commands.huggingface_cli download \ |
| hwk0809/RGBench-Cloth-Sim2Real-v1 --repo-type dataset \ |
| --local-dir ./data/sample |
| ``` |
|
|
| ## Licensing |
|
|
| - **Data**: CC-BY 4.0 — attribution required, commercial use permitted. |
| - **Code (benchmark)**: MIT — see the [RGBench repo](https://github.com/hwk0809/RGBench). |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the AAAI 2026 paper: |
|
|
| ```bibtex |
| @inproceedings{hu2026rgbench, |
| title = {Real Garment Benchmark ({RGBench}): A Comprehensive Benchmark for Robotic Garment Manipulation featuring a High-Fidelity Scalable Simulator}, |
| author = {Hu, Wenkang and Tang, Xincheng and E, Yanzhi and Li, Yitong and Shu, Zhengjie and Li, Wei and Wang, Huamin and Yang, Ruigang}, |
| booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, |
| year = {2026}, |
| url = {https://rgbench.github.io/} |
| } |
| ``` |
|
|
