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--- |
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tags: |
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- robotics |
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- grasping |
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- simulation |
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- nvidia |
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task_categories: |
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- other |
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- robotics |
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license: cc-by-4.0 |
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--- |
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# GraspGen: Scaling Sim2Real Grasping |
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GraspGen is a large-scale simulated grasp dataset for multiple robot embodiments and grippers. |
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<img src="assets/cover.png" width="1000" height="250" title="readme1"> |
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We release over 57 million grasps, computed for a subset of 8515 objects from the [Objaverse XL](https://objaverse.allenai.org/) (LVIS) dataset. These grasps are specific to three grippers: Franka Panda, the Robotiq-2f-140 industrial gripper, and a single-contact suction gripper (30mm radius). |
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<img src="assets/montage2.png" width="1000" height="500" title="readme2"> |
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## Dataset Format |
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The dataset is released in the [WebDataset](https://github.com/webdataset/webdataset) format. The folder structure of the dataset is as follows: |
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``` |
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grasp_data/ |
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franka/shard_{0-7}.tar |
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robotiq2f140/shard_{0-7}.tar |
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suction/shard_{0-7}.tar |
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splits/ |
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franka/{train/valid}_scenes.json |
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robotiq2f140/{train/valid}_scenes.json |
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suction/{train/valid}_scenes.json |
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``` |
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We release test-train splits along with the grasp dataset. The splits are made randomly based on object instances. |
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Each json file in the shard has the following data in a python dictionary. Note that `num_grasps=2000` per object. |
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``` |
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‘object’/ |
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‘scale’ # This is the scale of the asset, float |
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‘grasps’/ |
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‘object_in_gripper’ # boolean mask indicating grasp success, [num_grasps X 1] |
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‘transforms’ # Pose of the gripper in homogenous matrices, [num_grasps X 4 X 4] |
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``` |
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The coordinate frame convention for the three grippers are provided below: |
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<img src="assets/grippers.png" width="450" height="220" title="readme3"> |
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## Visualizing the dataset |
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We have provided some minimal, standalone scripts for visualizing this dataset. See the header of the [visualize_dataset.py](scripts/visualize_dataset.py) for installation instructions. |
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Before running any of the visualization scripts, remember to start meshcat-server in a separate terminal: |
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``` shell |
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meshcat-server |
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``` |
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To visualize a single object from the dataset, alongside its grasps: |
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```shell |
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cd scripts/ && python visualize_dataset.py --dataset_path /path/to/dataset --object_uuid {object_uuid} --object_file /path/to/mesh --gripper_name {choose from: franka, suction, robotiq2f140} |
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``` |
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To sequentially visualize a list of objects with its grasps: |
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```shell |
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cd scripts/ && python visualize_dataset.py --dataset_path /path/to/dataset --uuid_list {path to a splits.json file} --uuid_object_paths_file {path to a json file mapping uuid to absolute path of meshes} --gripper_name {choose from: franka, suction, robotiq2f140} |
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``` |
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## Objaverse dataset |
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Please download the Objaverse XL (LVIS) objects separately. See the helper script [download_objaverse.py](scripts/download_objaverse.py) for instructions and usage. |
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Note that running this script autogenerates a file that maps from `UUID` to the asset mesh path, which you can pass in as input `uuid_object_paths_file` to the `visualize_dataset.py` script. |
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## License |
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License Copyright © 2025, NVIDIA Corporation & affiliates. All rights reserved. |
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Both the dataset and visualization code is released under a CC-BY 4.0 [License](LICENSE_DATASET). |
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For business inquiries, please submit the form [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/). |
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## Contact |
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Please reach out to [Adithya Murali](http://adithyamurali.com) ([email protected]) and [Clemens Eppner](https://clemense.github.io/) ([email protected]) for further enquiries. |
