On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning
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Published:2024-01-24
Issue:2
Volume:11
Page:108
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ISSN:2306-5354
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Container-title:Bioengineering
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language:en
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Short-container-title:Bioengineering
Author:
Odeyemi Jethro1, Ogbeyemi Akinola1, Wong Kelvin1, Zhang Wenjun1ORCID
Affiliation:
1. Advanced Engineering Design Laboratory, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
Abstract
Prosthetic technology has witnessed remarkable advancements, yet challenges persist in achieving autonomous grasping control while ensuring the user’s experience is not compromised. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge and improve the autonomy of prosthetics, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that an object’s physical characteristics can affect the agent’s ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object-gripping capabilities.
Funder
NSERC (Natural Sciences and Engineering Research Council of Canada) CREATE (Collaborative Research and Training Experience) program
Reference33 articles.
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