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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

Recognition of Grasping Patterns Using Deep Learning for Human-Robot Collaboration.

Pedro Amaral¹, Filipe Silva¹, Vítor Santos²

¹Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This summary is machine-generated.

This study introduces a new AI framework for assistive robots to recognize objects held by human operators using hand and finger joint data. The research compares deep learning models, showing effective object recognition for improved human-robot collaboration.

Keywords:

collaborative robotics grasping posture hand–object interaction keypoints classification object recognition

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Collaborative robots require prediction and anticipation abilities for shared tasks.
Accurate object recognition by robots is crucial for understanding human operator intentions.

Purpose of the Study:

To develop a learning-based framework for assistive robots to recognize grasped objects using hand and finger joint patterns.
To compare the performance of Convolutional Neural Networks (CNNs) and Transformers for object recognition.

Main Methods:

Utilized MediaPipe for detecting hand landmarks from RGB images.
Developed a deep multi-class classifier to predict objects from extracted keypoints.
Evaluated CNN and Transformer architectures based on accuracy, precision, recall, and F1-score.

Main Results:

The proposed framework demonstrated effective object recognition capabilities.
Both CNN and Transformer models showed varying performance metrics.
Insights were gained into factors affecting model generalization.

Conclusions:

The developed framework successfully enables assistive robots to recognize objects manipulated by humans.
The study provides a comparative analysis of deep learning architectures for this task.
Findings highlight the importance of generalization in human-robot interaction systems.