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JiayiChenPKU
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BODex

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The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 299, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 353, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 304, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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[ICRA 2025] BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization.

Project page | Paper | Grasp synthesis code | Grasp validation code | Object pre-processing code | Learning code

Dataset structure:

object_assets
|- DGN_2k_processed.tar.gz  # Our pre-proposed meshes and training splits.
|- DGN_2k_vision.tar.gz     # The single-view point clouds. Only used for training networks. 
|_ DGN_obj_raw.zip        # The raw object meshes before pre-processing. Not needed unless you want to process by yourself. 

synthesized_grasps        # Successful grasps validated in MuJoCo 
|- allegro.tar.gz            
|- leap.tar.gz
|- shadow.tar.gz        # The above three can be used in DexLearn
|_ ur10e_shadow.zip       # Each grasp also includes a collision-free approaching trajectory with the table from a fixed initial state. The Shadow Hand is mounted on a UR10e arm.
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