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arXiv · 2502.04873

Training-free Task-oriented Grasp Generation

Jiaming Wang·Diwen Liu·Jizhuo Chen·Harold Soh
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Abstract

This paper presents a training-free pipeline for task-oriented grasp generation that combines pre-trained grasp generation models with vision-language models (VLMs). Unlike traditional approaches that focus solely on stable grasps, our method incorporates task-specific requirements by leveraging the semantic reasoning capabilities of VLMs. We evaluate five querying strategies, each utilizing different visual representations of candidate grasps, and demonstrate significant improvements over a baseline method in both grasp success and task compliance rates, with absolute gains of up to 36.9\% in overall success rate. Our results underline the potential of VLMs to enhance task-oriented manipulation, providing insights for future research in robotic grasping and human-robot interaction.

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Jiaming Wang, Diwen Liu, Jizhuo Chen, Harold Soh. 2025-02-07. Training-free Task-oriented Grasp Generation. https://arxiv.org/abs/2502.04873

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