A High-Efficient Reinforcement Learning Approach for Dexterous Manipulation.

ABSTRACT: Robotic hands have the potential to perform complex tasks in unstructured environments owing to their bionic design, inspired by the most agile biological hand. However, the modeling, planning and control of dexterous hands remain unresolved, open challenges, resulting in the simple movements and relatively clumsy motions of current robotic end effectors. This paper proposed a dynamic model based on generative adversarial architecture to learn the state mode of the dexterous hand, reducing the model's prediction error in long spans. An adaptive trajectory planning kernel was also developed to generate High-Value Area Trajectory (HVAT) data according to the control task and dynamic model, with adaptive trajectory adjustment achieved by changing the Levenberg-Marquardt (LM) coefficient and t

SUBMITTER: Zhang J 

PROVIDER: S-EPMC10296228 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature