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Related Experiment Video

Updated: Nov 19, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Affordance-Based Grasping Point Detection Using Graph Convolutional Networks for Industrial Bin-Picking Applications.

Ander Iriondo¹, Elena Lazkano², Ander Ansuategi¹

¹Department of Autonomous and Intelligent Systems, Fundación Tekniker, Iñaki Goenaga, 5-20600 Eibar, Spain.

Sensors (Basel, Switzerland)

|February 3, 2021

Summary

This summary is machine-generated.

This study introduces Graph Convolutional Networks (GCNs) for robotic grasping point detection in bin-picking. The new method leverages 3D point clouds to improve object affordance prediction for both suction and gripper end effectors.

Keywords:

affordance grasping deep learning graph convolutional network grasping point detection pick and place

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Area of Science:

Robotics
Computer Vision
Machine Learning

Background:

Grasping point detection is crucial for robotics and computer vision.
Deep learning methods show promise for predicting grasping points, especially for object affordances in bin-picking.
Existing methods often rely on RGB/RGB-D images, with limited clarity on 3D spatial information utilization.

Purpose of the Study:

To adapt and apply Deep Graph Convolutional Networks (GCNs) for predicting object affordances using n-dimensional point clouds.
To investigate the performance of GCNs for grasping point detection in an industrial bin-picking context for both suction and gripper end effectors.
To develop a novel bin-picking oriented data preprocessing pipeline.

Main Methods:

Adapted a Deep Graph Convolutional Network (GCN) model to process n-dimensional point clouds.
Developed a specialized data preprocessing pipeline tailored for bin-picking applications.
Created and utilized a high-accuracy RGB-D/3D dataset for model training.

Main Results:

The GCN-based method achieved improved top-1 precision scores compared to a 2D Fully Convolutional Network (FCN).
Achieved a 1.8% improvement in top-1 precision for suction grasping.
Achieved a 1.7% improvement in top-1 precision for gripper grasping.

Conclusions:

GCNs offer a performance boost for predicting object affordances in point clouds for robotic grasping.
This research presents the first application of GCNs for predicting affordances for suction and gripper end effectors in industrial bin-picking.
The developed method and dataset provide a flexible and effective solution for bin-picking applications.