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Updated: Jul 15, 2025

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

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

Published on: April 21, 2023

Robotic Grasp Detection Network Based on Improved Deformable Convolution and Spatial Feature Center Mechanism.

Miao Zou¹, Xi Li^1,2, Quan Yuan²

¹School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

Biomimetics (Basel, Switzerland)

|September 27, 2023

Summary

This summary is machine-generated.

This study introduces DCSFC-Grasp, a novel network for precise grasp detection on unidentified objects. It achieves high accuracy, outperforming existing models in both dataset benchmarks and real-world robotic arm experiments.

Keywords:

deformable convolution grasp detection robotic arm spatial feature center mechanism

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Area of Science:

Robotics
Computer Vision
Machine Learning

Background:

Precise grasp detection is crucial for robotic manipulation of unidentified objects.
Existing methods face challenges in handling multiscale features and global dependencies.

Purpose of the Study:

To propose an effective grasp detection network, DCSFC-Grasp, for precise grasping of unknown objects.
To improve accuracy and robustness in robotic grasping tasks.

Main Methods:

Introduced improved deformable convolution for adaptive multiscale feature extraction.
Employed a spatial feature center (SFC) layer with a multilayer perceptron (MLP) for global dependencies.
Utilized a learnable feature center (LFC) mechanism to aggregate local features.
Developed a lightweight CARAFE operator for feature upsampling.

Main Results:

Achieved high accuracy: 99.3% on the Cornell grasp dataset and 96.1% on the Jacquard grasp dataset.
Outperformed existing state-of-the-art grasp detection models.
Demonstrated effectiveness and robustness in real-world experiments using a six-DoF robotic arm.

Conclusions:

DCSFC-Grasp offers a highly accurate and robust solution for grasp detection.
The proposed network effectively handles multiscale features and global dependencies for precise robotic grasping.