NDPP-Grasp: Non-Differentiable Physical Plausibility Constraint-Guided Task-Oriented Dexterous Grasp Generation
NDPP-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.
The paper introduces NDPP-Grasp, a framework that integrates non-differentiable physical plausibility constraints into the denoising process of a diffusion model for task-oriented grasp generation, aiming to improve physical validity during synthesis rather than relying on post-hoc refinement.
Source: arXiv Robotics — research abstracts · Read original article ↗
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rocess of a task-aligned grasp diffusion model in a practical and effective manner, even when physical plausibility constraints are non-differentiable. This allows physical plausibility to shape grasp generation throughout denoising while preserving task alignment. Extensive experiments demonstrate the efficacy of our framework.
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Source：arXiv Robotics — research abstracts · arxiv.org