# 6-DoF Contrastive Grasp Proposal Network

arXiv: https://arxiv.org/abs/2103.15995 · HTML: https://mlsanity.com/paper/2103.15995/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2021.

## Built on by (most cited first, 12 of 12)

- [GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping](https://mlsanity.com/paper/2403.09637/) (2024)
- [Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks](https://mlsanity.com/paper/2312.06876/) (2023)
- [MonoGraspNet: 6-DoF Grasping with a Single RGB Image](https://mlsanity.com/paper/2209.13036/) (2022)
- [When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection](https://mlsanity.com/paper/2202.11911/) (2022)
- [PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models](https://mlsanity.com/paper/2402.16836/) (2024)
- [Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework](https://mlsanity.com/paper/2306.01839/) (2023)
- [Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection](https://mlsanity.com/paper/2312.10571/) (2023)
- [KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D input](https://mlsanity.com/paper/2303.05617/) (2023)
- [Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints](https://mlsanity.com/paper/2203.15155/) (2022)
- [VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction](https://mlsanity.com/paper/2203.04874/) (2022)
- [NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields](https://mlsanity.com/paper/2411.02482/) (2024)
- [MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping](https://mlsanity.com/paper/2507.02672/) (2025)

## Abstract (arXiv)

Proposing grasp poses for novel objects is an essential component for any robot manipulation task. Planning six degrees of freedom (DoF) grasps with a single camera, however, is challenging due to the complex object shape, incomplete object information, and sensor noise. In this paper, we present a 6-DoF contrastive grasp proposal network (CGPN) to infer 6-DoF grasps from a single-view depth image. First, an image encoder is used to extract the feature map from the input depth image, after which 3-DoF grasp regions are proposed from the feature map with a rotated region proposal network. Feature vectors that within the proposed grasp regions are then extracted and refined to 6-DoF grasps. The proposed model is trained offline with synthetic grasp data. To improve the robustness in reality and bridge the simulation-to-real gap, we further introduce a contrastive learning module and variant image processing techniques during the training. CGPN can locate collision-free grasps of an object using a single-view depth image within 0.5 seconds. Experiments on a physical robot further demonstrate the effectiveness of the algorithm.