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

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Published on: April 21, 2023

Pixel-Reasoning-Based Robotics Fine Grasping for Novel Objects with Deep EDINet Structure.

Chaoquan Shi¹, Chunxiao Miao¹, Xungao Zhong¹

¹School of Electrical Enginnering and Automation, Xiamen University of Technology, Xiamen 361024, China.

Sensors (Basel, Switzerland)

|June 10, 2022

Summary

This study introduces a novel pixel-level grasp detection method for robotics using the EDINet neural network. This approach enhances grasping accuracy and speed, outperforming existing algorithms in cluttered environments.

Keywords:

EDINet deep network pixel-level reasoning robotics fine grasping

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Area of Science:

Robotics
Computer Vision
Machine Learning

Background:

Traditional robotics grasp detection relies on time-consuming rectangle extraction, potentially missing optimal grasp configurations.
Existing methods struggle with gripper approaching conflicts and adaptability in cluttered environments with unknown objects.

Purpose of the Study:

To propose a novel pixel-level grasp detection method for RGB-D images.
To enhance the speed, robustness, and applicability of grasp detection in robotics.

Main Methods:

Introduced a fine grasping representation for parallel-jaw grippers, resolving conflicts and improving adaptability.
Utilized adaptive grasping width for precise object attribute representation.
Developed the Encoder-Decoder-Inception Network (EDINet) for predicting fine grasping configurations.

Main Results:

EDINet achieved 98.9% and 96.1% test accuracy on the Cornell and Jacquard datasets, respectively.
Demonstrated an average grasp success rate of 97.2% in single-object scenes and 93.7% in cluttered scenes.
The EDINet pipeline completes grasp detection in just 25 ms.

Conclusions:

The proposed EDINet method significantly improves pixel-level grasp detection accuracy and speed.
EDINet offers superior performance compared to state-of-the-art algorithms, especially in complex, cluttered scenarios.
This method enhances the practical application of robotic grasping for unknown objects.