Curriculum-reinforcement learning on simulation platform of tendon-driven high-degree of freedom underactuated manipulator
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Published:2023-07-12
Issue:
Volume:10
Page:
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ISSN:2296-9144
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Container-title:Frontiers in Robotics and AI
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language:
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Short-container-title:Front. Robot. AI
Author:
Or Keung,Wu Kehua,Nakano Kazashi,Ikeda Masahiro,Ando Mitsuhito,Kuniyoshi Yasuo,Niiyama Ryuma
Abstract
A high degree of freedom (DOF) benefits manipulators by presenting various postures when reaching a target. Using a tendon-driven system with an underactuated structure can provide flexibility and weight reduction to such manipulators. The design and control of such a composite system are challenging owing to its complicated architecture and modeling difficulties. In our previous study, we developed a tendon-driven, high-DOF underactuated manipulator inspired from an ostrich neck referred to as the Robostrich arm. This study particularly focused on the control problems and simulation development of such a tendon-driven high-DOF underactuated manipulator. We proposed a curriculum-based reinforcement-learning approach. Inspired by human learning, progressing from simple to complex tasks, the Robostrich arm can obtain manipulation abilities by step-by-step reinforcement learning ranging from simple position control tasks to practical application tasks. In addition, an approach was developed to simulate tendon-driven manipulation with a complicated structure. The results show that the Robostrich arm can continuously reach various targets and simultaneously maintain its tip at the desired orientation while mounted on a mobile platform in the presence of perturbation. These results show that our system can achieve flexible manipulation ability even if vibrations are presented by locomotion.
Funder
Japan Society for the Promotion of Science
Publisher
Frontiers Media SA
Subject
Artificial Intelligence,Computer Science Applications
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