# Instance-wise Grasp Synthesis for Robotic Grasping

arXiv: https://arxiv.org/abs/2302.07824 · HTML: https://mlsanity.com/paper/2302.07824/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2023.

## Built on by (most cited first, 5 of 5)

- [Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter](https://mlsanity.com/paper/2311.05779/) (2023)
- [Towards Open-World Grasping with Large Vision-Language Models](https://mlsanity.com/paper/2406.18722/) (2024)
- [Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulation](https://mlsanity.com/paper/2512.21065/) (2025)
- [RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation](https://mlsanity.com/paper/2603.14880/) (2026)
- [SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes](https://mlsanity.com/paper/2508.20547/) (2025)

## Abstract (arXiv)

Generating high-quality instance-wise grasp configurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance for robot manipulation tasks. This work proposed a novel Single-Stage Grasp (SSG) synthesis network, which performs high-quality instance-wise grasp synthesis in a single stage: instance mask and grasp configurations are generated for each object simultaneously. Our method outperforms state-of-the-art on robotic grasp prediction based on the OCID-Grasp dataset, and performs competitively on the JACQUARD dataset. The benchmarking results showed significant improvements compared to the baseline on the accuracy of generated grasp configurations. The performance of the proposed method has been validated through both extensive simulations and real robot experiments for three tasks including single object pick-and-place, grasp synthesis in cluttered environments and table cleaning task.