# Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter

arXiv: https://arxiv.org/abs/1904.07402 · HTML: https://mlsanity.com/paper/1904.07402/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping](https://mlsanity.com/paper/2103.12311/) (2021)
- [GKNet: grasp keypoint network for grasp candidates detection](https://mlsanity.com/paper/2106.08497/) (2021)
- [Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark](https://mlsanity.com/paper/2305.16378/) (2023)
- [Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking](https://mlsanity.com/paper/2307.16488/) (2023)
- [Good Grasps Only: A data engine for self-supervised fine-tuning of pose estimation using grasp poses for verification](https://mlsanity.com/paper/2409.11512/) (2024)
- [OptiGrasp: Optimized Grasp Pose Detection Using RGB Images for Warehouse Picking Robots](https://mlsanity.com/paper/2409.19494/) (2024)
- [Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors](https://mlsanity.com/paper/2304.10108/) (2023)
- [Diffusion Suction Grasping with Large-Scale Parcel Dataset](https://mlsanity.com/paper/2502.07238/) (2025)
- [Realtime Global Attention Network for Semantic Segmentation](https://mlsanity.com/paper/2112.12939/) (2021)
- [MultiGraspNet: A Multitask 3D Vision Model for Multi-gripper Robotic Grasping](https://mlsanity.com/paper/2602.06504/) (2026)

## Abstract (arXiv)

This paper focuses on robotic picking tasks in cluttered scenario. Because of the diversity of poses, types of stack and complicated background in bin picking situation, it is much difficult to recognize and estimate their pose before grasping them. Here, this paper combines Resnet with U-net structure, a special framework of Convolution Neural Networks (CNN), to predict picking region without recognition and pose estimation. And it makes robotic picking system learn picking skills from scratch. At the same time, we train the network end to end with online samples. In the end of this paper, several experiments are conducted to demonstrate the performance of our methods.