在模拟平台上进行课程增强学习,用于肌驱动的高自由度低调操纵器
Keung Or1, Kehua Wu2, Kazashi Nakano2
1School of Science and Technology, Meiji University, Kawasaki, Japan.
Frontiers in robotics and AI
|July 28, 2023
概括
这项研究介绍了Robostrich手臂,一个灵活的,高DOF操纵器. 使用基于课程的强化学习,它实现了精确的控制和稳定的操纵,即使在移动平台上.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 生物模拟学是一种生物模拟学.
背景情况:
- 高自由度 (DOF) 操纵器提供了增强的灵巧性,但提出了设计和控制的挑战.
- 肌驱动的,低调的系统提供灵活性和减重,非常适合复杂的操纵器.
- 灵感来自于鸟部的Robostrich手臂是一个新的肌驱动,高DOF低调的操纵器.
研究的目的:
- 为了解决Robostrich手臂的控制和模拟复杂性.
- 开发一种强化学习方法,以实现操纵能力.
- 在实际应用场景中验证操纵器的性能.
主要方法:
- 实施了基于课程的强化学习方法,模仿人类的学习进度.
- 开发了一种模拟方法,以模拟复杂结构中的肌驱动操纵.
- 机器人手臂与移动平台集成,用于测试扰动下的性能.
主要成果:
- 机器人手臂展示了连续的目标达到与精确的方向控制.
- 该系统成功地执行了操纵任务,尽管有来自机车运动的干扰.
- 基于课程的强化学习使人能够逐步掌握操纵技能.
结论:
- 开发的系统提供了一种可行的解决方案,用于控制高DOF低值操纵器.
- 罗博斯特里奇手臂表现出强大而灵活的操纵能力,即使在动态环境中.
- 这项研究通过仿生设计和先进的控制策略推进了机器人操纵领域.
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