Grasp detection requires flexibility to handle objects of various shapes without relying on prior knowledge of the object, while also offering intuitive, user-guided control. This paper introduces GraspSAM, an innovative extension of the Segment Anything Model (SAM), designed for prompt-driven and category-agnostic grasp detection. Unlike previous methods, which are often limited by small-scale training data, GraspSAM leverages the large-scale training and prompt-based segmentation capabilities of SAM to efficiently support both target-object and category-agnostic grasping. By utilizing adapters, learnable token embeddings, and a lightweight modified decoder, GraspSAM requires minimal fine-tuning to integrate object segmentation and grasp prediction into a unified framework. The model achieves state-of-the-art (SOTA) performance across multiple datasets, including Jacquard, Grasp-Anything, and Grasp-Anything++. Extensive experiments demonstrate the flexibility of GraspSAM in handling different types of prompts (such as points, boxes, and language), highlighting its robustness and effectiveness in real-world robotic applications.
@article{arxiv.2409.12521,
title = {GraspSAM: When Segment Anything Model Meets Grasp Detection},
author = {Sangjun Noh and Jongwon Kim and Dongwoo Nam and Seunghyeok Back and Raeyoung Kang and Kyoobin Lee},
journal= {arXiv preprint arXiv:2409.12521},
year = {2024}
}