BiDexGrasp supplies a 9.7-million-grasp bimanual dexterous dataset built via two-stage synthesis and a coordinated geometry-size-adaptive model that generates grasps for unseen objects.
DexVLG: Dexterous vision-language-grasp model at scale
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DreamVLA uses dynamic-region-guided world knowledge prediction, block-wise attention to disentangle information types, and a diffusion transformer for actions, reaching 76.7% success on real robot tasks and 4.44 average length on CALVIN ABC-D.
BLaDA grounds open-vocabulary language into functional dexterous manipulation via knowledge-guided parsing, triangular localization in 3DGS fields, and keypoint grasp execution.
GD2P generates and learns dexterous hand poses for nonprehensile pushing and pulling by combining contact-guided sampling, physics-based filtering, and a geometry-conditioned diffusion model, demonstrated on Allegro and LEAP hands in real-world tests.
AugVLA-3D augments existing VLA models with depth-derived 3D features and action priors to improve generalization and action accuracy in 3D robotic tasks.
citing papers explorer
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BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes
BiDexGrasp supplies a 9.7-million-grasp bimanual dexterous dataset built via two-stage synthesis and a coordinated geometry-size-adaptive model that generates grasps for unseen objects.
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DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
DreamVLA uses dynamic-region-guided world knowledge prediction, block-wise attention to disentangle information types, and a diffusion transformer for actions, reaching 76.7% success on real robot tasks and 4.44 average length on CALVIN ABC-D.
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BLaDA: Bridging Language to Functional Dexterous Actions within 3DGS Fields
BLaDA grounds open-vocabulary language into functional dexterous manipulation via knowledge-guided parsing, triangular localization in 3DGS fields, and keypoint grasp execution.
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Learning Geometry-Aware Nonprehensile Pushing and Pulling with Dexterous Hands
GD2P generates and learns dexterous hand poses for nonprehensile pushing and pulling by combining contact-guided sampling, physics-based filtering, and a geometry-conditioned diffusion model, demonstrated on Allegro and LEAP hands in real-world tests.
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AugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action Models
AugVLA-3D augments existing VLA models with depth-derived 3D features and action priors to improve generalization and action accuracy in 3D robotic tasks.