# GraspSAM: When Segment Anything Model Meets Grasp Detection

arXiv: https://arxiv.org/abs/2409.12521 · HTML: https://mlsanity.com/paper/2409.12521/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2024.

## Built on by (most cited first, 13 of 13)

- [VISO-Grasp: Vision-Language Informed Spatial Object-centric 6-DoF Active View Planning and Grasping in Clutter and Invisibility](https://mlsanity.com/paper/2503.12609/) (2025)
- [GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning](https://mlsanity.com/paper/2409.14403/) (2024)
- [StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models](https://mlsanity.com/paper/2603.07307/) (2026)
- [Unfettered Forceful Skill Acquisition with Physical Reasoning and Coordinate Frame Labeling](https://mlsanity.com/paper/2505.09731/) (2025)
- [Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulation](https://mlsanity.com/paper/2512.21065/) (2025)
- [GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors](https://mlsanity.com/paper/2606.08440/) (2026)
- [RoboEye: Enhancing 2D Robotic Object Identification with Selective 3D Geometric Keypoint Matching](https://mlsanity.com/paper/2509.14966/) (2025)
- [XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation](https://mlsanity.com/paper/2510.11036/) (2025)
- [VLAD-Grasp: Zero-shot Grasp Detection via Vision-Language Models](https://mlsanity.com/paper/2511.05791/) (2025)
- [Differentiable Inverse Graphics for Zero-shot Scene Reconstruction and Robot Grasping](https://mlsanity.com/paper/2602.05029/) (2026)
- [Speech2Grasp: Data-Efficient Transfer of Text-Conditioned Grasp Detection to Speech in Humanoid Robots](https://mlsanity.com/paper/2607.26567/) (2026)
- [Training-free Suction Grasp Detection for Deformed Aseptic Cartons Using Vision-Language Models and Geometric Surface Scoring](https://mlsanity.com/paper/2608.28246/) (2026)
- [RAGrasp: Geometry-Semantic Template Retrieval and Grasp Transfer](https://mlsanity.com/paper/2610.04438/) (2026)

## Abstract (arXiv)

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.