Odeyemi, J.; Ogbeyemi, A.; Wong, K.; Zhang, W. On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning. Bioengineering2024, 11, 108.
Odeyemi, J.; Ogbeyemi, A.; Wong, K.; Zhang, W. On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning. Bioengineering 2024, 11, 108.
Odeyemi, J.; Ogbeyemi, A.; Wong, K.; Zhang, W. On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning. Bioengineering2024, 11, 108.
Odeyemi, J.; Ogbeyemi, A.; Wong, K.; Zhang, W. On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning. Bioengineering 2024, 11, 108.
Abstract
This paper explores the application of machine learning techniques for automated object grasping. 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, 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 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.
Keywords
Computer vision; electromyography; hand gestures; machine learning; prosthetics
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
