Event
Dexterous Grasping Framework via Denoising with Non-Differentiable Physical Constraint Injection
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Qiuchi Xiang and co-authors propose NDPP-Grasp, which injects physical plausibility constraints directly into the denoising process of a task-aligned grasping diffusion model, enabling guidance to be applied throughout generation even when the constraints are non-differentiable. The work has been accepted to NeurIPS 2026. Existing diffusion methods first train a grasping diffusion model to align with the task, then perform physical plausibility correction after generation; the generation trajectory itself is not guided by physical constraints. NDPP-Grasp targets precisely this gap.
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- NDPP-Grasp: Non-Differentiable Physical Plausibility Constraints Guide Task-Oriented Dexterous Grasp Generation
NDPP-Grasp proposes injecting physical plausibility constraints directly into the denoising process of a task-aligned grasp diffusion model, so that guidance can be applied throughout generation even when the constraints are non-differentiable. Existing diffusion methods first train a grasp diffusion model for task alignment and then apply physical plausibility correction after generation, leaving the generation trajectory itself unguided by physical constraints. The work was accepted to NeurIPS 2026.
arXiv cs.RO ·
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