| --- |
| license: cc-by-nc-4.0 |
| pretty_name: GraspXL |
| size_categories: |
| - 10M<n<100M |
| tags: |
| - grasping |
| - dexterous-hand |
| - motion-generation |
| - objaverse |
| - physics-simulation |
| - robotics |
| - 3d |
| --- |
| |
| # GraspXL |
|
|
| GraspXL is a large-scale dataset of physically plausible dexterous grasping |
| motion sequences over Objaverse objects. It contains generated grasping motions |
| for different hand models, together with processed object meshes used by the |
| recorded sequences. |
|
|
| The main archives cover diverse hand-object interactions where hands approach |
| objects from different directions. The tabletop archives provide a complementary |
| setting where objects are placed on a table and hands approach from above, which |
| is useful for applications focused on tabletop grasping scenes. |
|
|
| ## Resources |
|
|
| - [Paper](https://arxiv.org/abs/2403.19649) |
| - [Project page](https://eth-ait.github.io/graspxl/) |
| - [Code](https://github.com/zdchan/GraspXL) |
| - [Visualizer](https://github.com/zdchan/GraspXL_visualization) |
|
|
| ## Highlights |
|
|
| - 10M+ diverse grasping motions for 500k+ objects and multiple dexterous hand |
| models. |
| - Motions generated with physics simulation to improve physical plausibility. |
| - Frame-level object and hand poses for each motion sequence. |
| - Both diverse-approach and tabletop grasping settings. |
|
|
| ## Potential Applications |
|
|
| - Generating large-scale pseudo 3D RGBD grasping motions with texture generation methods, which can support downstream applications such as |
| training pose estimation or mesh reconstruction models (e.g., [HGGT](https://lym29.github.io/HGGT/)). |
| - Simulating general human hand motions for human-robot interaction. |
| - Serving as expert demonstrations for imitation learning and generative models (e.g., [PAM](https://gasaiyu.github.io/PAM.github.io/)). |
| - Add precise finger motions for full-body grasping motion generation. |
| - Achieve zero-shot text-to-motion generation with off-the-shelf text-to-mesh |
| generation methods. |
|
|
| ## Archive Overview |
|
|
| The dataset is stored as `.zip` archives. To make downloading easier, recorded |
| motion sequences are split across several archives. For many objects, each |
| sequence archive contains several motion sequences, so users can choose which |
| archives to download. |
|
|
| | Archive | Content | |
| | --- | --- | |
| | `object_dataset.zip` | Processed object meshes used by the recorded sequences. | |
| | `objaverse_urdf.zip` | URDF files of the Objaverse objects used by the simulator. | |
| | `allegro_dataset_1.zip`, `allegro_dataset_2.zip` | Allegro Hand motions with diverse approach directions. | |
| | `mano_dataset_1.zip.part00`, `mano_dataset_1.zip.part01` | Split parts of `mano_dataset_1.zip`, containing MANO motions with diverse approach directions. | |
| | `mano_dataset_2.zip.part00`, `mano_dataset_2.zip.part01` | Split parts of `mano_dataset_2.zip`, containing additional MANO motions with diverse approach directions. | |
| | `mano_dataset_3.zip.part00`, `mano_dataset_3.zip.part01` | Split parts of `mano_dataset_3.zip`, containing additional MANO motions with diverse approach directions. | |
| | `leap_dataset_1.zip` | LEAP hand motions for a subset of the objects. | |
| | `allegro_tabletop.zip` | Allegro Hand tabletop grasping motions. | |
| | `mano_tabletop.zip` | MANO tabletop grasping motions. | |
| | `sharpa_tabletop.zip` | Sharpa Wave Hand tabletop grasping motions. | |
|
|
| ## Object Data |
|
|
| `object_dataset.zip` contains processed object meshes. The meshes are scaled and |
| decimated before use. |
|
|
| ```text |
| object_dataset.zip |
| |-- small |
| | |-- <object_id> |
| | | `-- <object_id>.obj |
| | `-- ... |
| |-- medium |
| | |-- <object_id> |
| | | `-- <object_id>.obj |
| | `-- ... |
| `-- large |
| |-- <object_id> |
| | `-- <object_id>.obj |
| `-- ... |
| ``` |
|
|
| `small`, `medium`, and `large` contain object meshes with different scales used |
| by the recorded sequences. Please check the paper for more details. |
|
|
| `objaverse_urdf.zip` contains the corresponding URDF files for the Objaverse |
| objects. These URDF files are used by the simulation code to generate motions. |
|
|
| ## Motion Archives |
|
|
| All motion archives use the same high-level layout: |
|
|
| ```text |
| <motion_archive>.zip |
| |-- small |
| | |-- <object_id> |
| | | |-- <hand_prefix>_0.npy |
| | | |-- <hand_prefix>_1.npy |
| | | `-- ... |
| | `-- ... |
| |-- medium |
| | |-- <object_id> |
| | | |-- <hand_prefix>_0.npy |
| | | `-- ... |
| | `-- ... |
| `-- large |
| |-- <object_id> |
| | |-- <hand_prefix>_0.npy |
| | `-- ... |
| `-- ... |
| ``` |
|
|
| Not every object has the same number of recorded sequences. |
|
|
| ### Diverse-Approach Motions |
|
|
| These archives contain motions where hands approach objects from diverse |
| directions to generate broad hand-object interaction coverage. |
|
|
| | Archive | Hand model | File prefix | Hand pose shape | |
| | --- | --- | --- | --- | |
| | `allegro_dataset_1.zip`, `allegro_dataset_2.zip` | Allegro | `allegro_*.npy` | `(frame_num, 22)` | |
| | `mano_dataset_1.zip`, `mano_dataset_2.zip`, `mano_dataset_3.zip` | MANO | `mano_*.npy` | `(frame_num, 45)` | |
| | `leap_dataset_1.zip` | LEAP | `leap_*.npy` | `(frame_num, 22)` | |
|
|
| The MANO archives are stored as split files to keep each repository file under |
| the hosting file-size limit. Reconstruct them before extracting: |
|
|
| ```bash |
| cat mano_dataset_1.zip.part00 mano_dataset_1.zip.part01 > mano_dataset_1.zip |
| cat mano_dataset_2.zip.part00 mano_dataset_2.zip.part01 > mano_dataset_2.zip |
| cat mano_dataset_3.zip.part00 mano_dataset_3.zip.part01 > mano_dataset_3.zip |
| ``` |
|
|
| ### Tabletop Motions |
|
|
| These archives contain motions in a tabletop setting. Objects are placed on a |
| table and the hand approaches from above. The tabletop setting uses part of the |
| method from RobustDexGrasp and is intended for applications that need tabletop |
| grasping data. |
|
|
| | Archive | Hand model | File prefix | Hand pose shape | |
| | --- | --- | --- | --- | |
| | `allegro_tabletop.zip` | Allegro | `allegro_*.npy` | `(frame_num, 22)` | |
| | `mano_tabletop.zip` | MANO | `mano_*.npy` | `(frame_num, 45)` | |
| | `sharpa_tabletop.zip` | Sharpa Wave | `sharpa_*.npy` | `(frame_num, 28)` | |
|
|
| ## Sequence Format |
|
|
|
|
| ### Common Fields |
|
|
| Each `.npy` file contains a single motion sequence: |
|
|
| ```python |
| data = np.load("<sequence>.npy", allow_pickle=True).item() |
| ``` |
|
|
| The sequence dictionary contains a `right_hand` entry and one object entry keyed |
| by `<object_id>`. |
|
|
| - `data["right_hand"]["trans"]`: NumPy array of shape `(frame_num, 3)`. Wrist |
| position sequence. |
| - `data["right_hand"]["rot"]`: NumPy array of shape `(frame_num, 3)`. Wrist |
| orientation sequence in axis-angle representation. |
| - `data["right_hand"]["pose"]`: Hand-model-specific pose sequence. See the |
| archive tables above for the pose dimensionality. |
| - `data[object_id]["trans"]`: NumPy array of shape `(frame_num, 3)`. Object |
| position sequence. |
| - `data[object_id]["rot"]`: NumPy array of shape `(frame_num, 3)`. Object |
| orientation sequence in axis-angle representation. |
|
|
|
|
| ### Pose Conventions |
|
|
| - Diverse-approach archives also include `data[object_id]["angle"]`, which is |
| a reserved field and is not used. |
| - The actual wrist pose is always represented by |
| `data["right_hand"]["trans"]` and `data["right_hand"]["rot"]`. For non-MANO robotic dexterous hands, the first 6 dimensions of `data["right_hand"]["pose"]` are virtual wrist joint values not used for the actual wrist pose. The remaining dimensions are the hand joint angles. |
| - For MANO, `data["right_hand"]["rot"]` and `data["right_hand"]["pose"]` |
| concatenate to the original MANO pose parameter. The diverse-approach MANO |
| archives use `flat_hand_mean=False`, while the tabletop MANO archive uses |
| `flat_hand_mean=True` with an additional `wrist_bias` applied to `data["right_hand"]["trans"]`. |
|
|
| ## Data Loading |
|
|
| For more detailed data loading and visualization examples, please refer to the |
| [visualizer repository](https://github.com/zdchan/GraspXL_visualization). |
|
|
| ```python |
| import numpy as np |
| |
| data = np.load("allegro_0.npy", allow_pickle=True).item() |
| object_id = next(key for key in data.keys() if key != "right_hand") |
| |
| right_hand_trans = data["right_hand"]["trans"] |
| right_hand_rot = data["right_hand"]["rot"] |
| right_hand_pose = data["right_hand"]["pose"] |
| |
| object_trans = data[object_id]["trans"] |
| object_rot = data[object_id]["rot"] |
| ``` |
|
|
| ## Citation |
|
|
| If you use GraspXL, please cite: |
|
|
| ```bibtex |
| @inProceedings{zhang2024graspxl, |
| title={{GraspXL}: Generating Grasping Motions for Diverse Objects at Scale}, |
| author={Zhang, Hui and Christen, Sammy and Fan, Zicong and Hilliges, Otmar and Song, Jie}, |
| booktitle={European Conference on Computer Vision (ECCV)}, |
| year={2024} |
| } |
| ``` |
|
|
| The tabletop setting uses part of the method from RobustDexGrasp. If you find |
| this setting useful, please consider citing: |
|
|
| ```bibtex |
| @inproceedings{zhang2025RobustDexGrasp, |
| title={{RobustDexGrasp}: Robust Dexterous Grasping of General Objects}, |
| author={Zhang, Hui and Wu, Zijian and Huang, Linyi and Christen, Sammy and Song, Jie}, |
| booktitle={Conference on Robot Learning (CoRL)}, |
| year={2025} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the |
| [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/). |
|
|
