aiqbench analysis
This arXiv paper proposes a way to steer a diffusion model's denoising process with physical-plausibility guidance, including constraints that cannot be differentiated. That addresses a common weakness of current robotic grasp generators, which refine grasps only after they are produced. The abstract reports "extensive experiments" but gives no benchmarks or quantitative results, so the practical gain is unverified. The idea of non-differentiable guidance during sampling may transfer to other constrained generation tasks.
NDPP-Grasp: Non-Differentiable Physical Plausibility Constraint-Guided Task-Oriented Dexterous Grasp Generation
arXiv:2606.02432v2 Announce Type: replace Abstract: Task-oriented dexterous grasp generation aims to produce dexterous grasp poses that are both physically plausible and functionally suitable for specified manipulation tasks. Existing diffusion-based methods often address these two requirements in a decoupled manner: they first train a grasp diffusion model for task alignment and then rely on post-generation refinement to improve physical plausibility. However, this after-the-fact correction strategy applies physical plausibility guidance only once the grasp has already been generated, leaving the generation trajectory itself unguided by physical constraints and potentially leading to suboptimal grasps. To address this problem, we propose a novel framework that directly injects physical plausibility guidance into the denoising process 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.
Abstract:Task-oriented dexterous grasp generation aims to produce dexterous grasp poses that are both physically plausible and functionally suitable for specified manipulation tasks. Existing diffusion-based methods often address these two requirements in a decoupled manner: they first train a grasp diffusion model for task alignment and then rely on post-generation refinement to improve physical plausibility. However, this after-the-fact correction strategy applies physical plausibility guidance only once the grasp has already been generated, leaving the generation trajectory itself unguided by physical constraints and potentially leading to suboptimal grasps. To address this problem, we propose a novel framework that directly injects physical plausibility guidance into the denoising process 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.
Robotics (cs.RO)
arXiv:2606.02432 [cs.RO]
(or arXiv:2606.02432v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.02432 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 237 words