Abstract
Investigators discuss new findings in artificial intelligence. According to news reporting originating from Saskatoon, Canada, by NewsRx correspondents, research stated, “Prosthetic technology has witnessed remarkable advancements, yet challenges persist in achieving autonomous grasping control while ensuring the user’s experience is not compromised.” Funders for this research include Nserc (Natural Sciences And Engineering Research Council of Canada) Create (Collaborative Research And Training Experience) Program. The news correspondents obtained a quote from the research from University of Saskatchewan: “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 ActorCritic (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.”
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