NDPP-Grasp: Non-Differentiable Physical Plausibility Constraint-Guided Task-Oriented Dexterous Grasp Generation
Overview
This research presents NDPP-Grasp, a novel framework for generating task-oriented dexterous grasps that are physically plausible. Unlike existing diffusion-based methods that decouple task alignment from physical plausibility and rely on post-generation refinement, NDPP-Grasp directly injects non-differentiable physical plausibility guidance into the denoising process of a task-aligned grasp diffusion model. This approach ensures that physical constraints shape the grasp generation trajectory throughout the entire denoising phase, preventing suboptimal grasps caused by unguided generation. Extensive experiments demonstrate the efficacy of this framework in producing grasps that are both functionally suitable and physically valid.
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- arXiv Robotics — research abstractsSignalNDPP-Grasp: Non-Differentiable Physical Plausibility Constraint-Guided Task-Oriented Dexterous Grasp Generation
This research presents NDPP-Grasp, a novel framework for generating task-oriented dexterous grasps that are physically plausible. Unlike existing diffusion-based methods that decouple task alignment from physical plausibility and rely on post-generation refinement, NDPP-Grasp directly injects non-differentiable physical plausibility guidance into the denoising process of a task-aligned grasp diffusion model. This approach ensures that physical constraints shape the grasp generation trajectory throughout the entire denoising phase, preventing suboptimal grasps caused by unguided generation. Extensive experiments demonstrate the efficacy of this framework in producing grasps that are both functionally suitable and physically valid.
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Current attention 3·Peak within the comparable range 7（2026-11-02 18:00 UTC）·Change within the comparable range over 24 hours -51%
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