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GripDepthSense3DNet: A Depth-Enabled Hardness Sensing Framework in Soft Robotic Grasping.

Ting Rang Ling¹, Bryan Jun Sheng Lee², Chee Pin Tan¹

¹Department of Electrical and Robotics Engineering, School of Engineering, Monash University Malaysia, Subang Jaya, Malaysia.

|April 15, 2025

Summary

This summary is machine-generated.

This study introduces GripDepthSense3DNet, a machine learning model using 3D depth sensing for accurate object hardness detection during grasping. The novel network efficiently senses hardness in deformable objects, outperforming existing methods.

Keywords:

convolutional neural networks depth camera hardness estimation robotic grippers soft robots

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Area of Science:

Robotics
Computer Vision
Machine Learning

Background:

Soft grippers are crucial for handling deformable objects, but accurate hardness sensing remains a significant challenge.
Hardness sensing is vital for applications like fruit ripeness assessment, food quality control, and product sorting.

Purpose of the Study:

To develop an innovative approach for accurate hardness sensing during grasping using 3D depth sensing and machine learning.
To introduce GripDepthSense3DNet, a novel network capable of capturing spatial-temporal deformation features for hardness estimation.

Main Methods:

A novel network, GripDepthSense3DNet, was developed integrating 3D depth sensing with machine learning.
The network was trained on a dataset of depth images capturing object deformation.
Spatial-temporal deformation features were extracted from a series of depth images to estimate hardness.

Main Results:

GripDepthSense3DNet achieved a mean absolute percentage error of 0.46% for trained shapes and hardness.
The model demonstrated significant efficiency, with a 94.8% reduction in parameters and 92.9% shorter training time compared to ResNet-50.
Optimal depth ranges and intervals were identified through systematic study.

Conclusions:

GripDepthSense3DNet offers a highly accurate and efficient solution for hardness sensing in deformable objects.
The network's dynamic tuning capability allows for seamless adaptation to new shapes, hardness levels, and arbitrary objects, showcasing its versatility.