1. GraspSAM: When Segment Anything Model Meets Grasp Detection
- Author
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Noh, Sangjun, Kim, Jongwon, Nam, Dongwoo, Back, Seunghyeok, Kang, Raeyoung, and Lee, Kyoobin
- Subjects
Computer Science - Robotics ,Electrical Engineering and Systems Science - Systems and Control - Abstract
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., Comment: 6 pages (main), 1 page (references)
- Published
- 2024