Datasets:
|
Download README.md from GeorgiaTech/PhysCoRe: direct link, hf CLI and curl.
- Browser
- Download file 5.52 kB
-
https://huggingface.co/datasets/GeorgiaTech/PhysCoRe/resolve/main/README.md
- Command line
-
hf download hf://datasets/GeorgiaTech/PhysCoRe/README.md
-
curl -L -o README.md https://huggingface.co/datasets/GeorgiaTech/PhysCoRe/resolve/main/README.md
5.52 kB
| license: other | |
| license_name: mixed-mit-and-cc-by-4.0 | |
| license_link: LICENSE | |
| pretty_name: PhysCoRe | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - robotics | |
| tags: | |
| - deformable-objects | |
| - robot-manipulation | |
| - material-estimation | |
| # PhysCoRe | |
| Multi-view RGB-D recordings of deformable objects being manipulated by hand, plus the | |
| trained model weights, 3D Gaussian splats and configs needed to reproduce the results in | |
| **PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable | |
| Dynamics** (CoRL 2026). | |
| - Project page: https://lunarlab-gatech.github.io/PhysCoRe-website/ | |
| - Paper: https://arxiv.org/abs/2607.20653 | |
| - Code: https://github.com/lunarlab-gatech/PhysCoRe | |
| ## Contents | |
| ``` | |
| data/ | |
| ├── phystwin.zip 14 cases, redistributed (see Licenses) | |
| └── physcore.zip 12 cases, recorded by us | |
| gaussian_output/physcore.zip static 3DGS scenes for the 12 physcore cases | |
| checkpoints/ MfM_checkpoint.pt, RfD_checkpoint.pt | |
| configs/ one YAML per pipeline script | |
| ``` | |
| The recordings are shipped as archives to keep the repository to a handful of files. | |
| Unzip each one in place; every archive expands into a directory of the same name: | |
| ```bash | |
| cd data && unzip phystwin.zip && unzip physcore.zip && cd .. | |
| cd gaussian_output && unzip physcore.zip && cd .. | |
| ``` | |
| which gives `data/phystwin/different_types/<case>/`, | |
| `data/physcore/different_types/<case>/` and `gaussian_output/physcore/<case>/`. | |
| Each of the 26 cases is one recording from **3 calibrated RGB-D cameras**, time-aligned: | |
| | path | content | | |
| |---|---| | |
| | `calibrate.pkl` | pickled list of 3 camera-to-world 4x4 matrices | | |
| | `metadata.json` | per-camera intrinsics, image size `WH`, `frame_num`, serial numbers | | |
| | `color/{0,1,2}/<frame>.png` | RGB frames | | |
| | `color/{0,1,2}.mp4` | per-camera RGB video | | |
| | `depth/{0,1,2}/<frame>.npy` | depth frames, uint16 millimeters | | |
| | `mask/{0,1,2}/<mask_id>/<frame>.png` | object and controller segmentation | | |
| | `mask/mask_info_{0,1,2}.json` | mask id to label mapping | | |
| | `sampled_tracks.pkl` | sampled 3D tracks | | |
| ## Where to start | |
| Unzip the three archives as above, then copy the contents of this repository into the | |
| root of a PhysCoRe checkout, so that | |
| `data/`, `gaussian_output/`, `checkpoints/` and `configs/` sit beside the pipeline | |
| scripts. The code repository's README walks through the stages in order and names the | |
| config each one reads. | |
| Because `mask/` and `sampled_tracks.pkl` are included, you can skip the segmentation and | |
| tracking stage and go straight to building episodes with | |
| `datagen/convert3d/convert_to_episode.py`. Run that earlier stage only if you want to | |
| reproduce it; it additionally needs the SAM 2 and GroundingDINO weights, which are not | |
| redistributed here. | |
| The `configs/` here carry the values used for the released results, with run directories | |
| left as `YYYYMMDD_hhmm` placeholders to be stamped at launch. Per-case controller contact | |
| radii in `per_sample_rollout_config` are scene-dependent and will need retuning for your | |
| own objects. | |
| ### Confidence overlay settings, per case | |
| `configs/render_MfM_confidence_3dgs.yaml` ships one example case, but the colormap ceiling | |
| has to be set per case. These are the values behind the released overlay videos, measured | |
| with `checkpoints/MfM_checkpoint.pt` on `data_episodes/physcore/<case>/episode_0000`: | |
| | case | `norm_hi` | `video_frames` | contact radius | | |
| |---|---|---|---| | |
| | `double_clift_cloth` | 2.0 | 120 | 0.06 (CLI override) | | |
| | `single_clift_cloth` | 1.75 | null | 0.044 (CLI override) | | |
| | `single_clift_rope` | 2.5 | null | 0.04 | | |
| | `double_squeeze_plastic` | 2.8 | null | 0.04 | | |
| | `double_stretch_bear_1` | 15.0 | null | 0.06 | | |
| | `single_push_rope` | 2.7 | 150 | 0.02 | | |
| The contact radius applies to the `validate_MfM.py` run that produces `render.traj_path`, | |
| not to the render itself. `configs/validate_MfM.yaml` already resolves to the value above | |
| for every case except the two marked *CLI override*, which need it passed on the command | |
| line: | |
| ```bash | |
| python validate_MfM.py --config configs/validate_MfM.yaml \ | |
| --root data_episodes/physcore/double_clift_cloth/episode_0000 \ | |
| rollout.manipulation_controller_grid_contact_radius=0.06 | |
| ``` | |
| ## Licenses | |
| This repository is **mixed-license**. Check the directory before reusing anything. | |
| | path | license | | |
| |---|---| | |
| | `data/phystwin/` | **MIT**, Copyright (c) 2025 Hanxiao Jiang — see `data/phystwin/LICENSE` | | |
| | `data/physcore/`, `gaussian_output/`, `checkpoints/`, `configs/` | **CC-BY-4.0**, Lunar Lab @ Georgia Tech | | |
| The 14 cases under `data/phystwin/` are redistributed from the | |
| [PhysTwin dataset](https://huggingface.co/datasets/Jianghanxiao/PhysTwin) under its MIT | |
| license, which permits redistribution provided the copyright notice is retained. The | |
| `mask/` and `sampled_tracks.pkl` files in those case directories are derived from those | |
| recordings and carry the same terms. If you use them, please cite PhysTwin as well as | |
| this work. | |
| ## Citation | |
| We hope this dataset is useful for your research. If it contributes to your work, please | |
| consider citing: | |
| ```bibtex | |
| @inproceedings{yin2026physcore, | |
| title = {PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics}, | |
| author = {Yin, Haocheng and Tao, Shuohan and Chen, Yongsheng and Gan, Lu}, | |
| booktitle = {Conference on Robot Learning (CoRL)}, | |
| series = {Proceedings of Machine Learning Research}, | |
| publisher = {PMLR}, | |
| year = {2026} | |
| } | |
| ``` | |