BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization
Paper • 2412.16490 • Published
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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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