软机器人系统的视觉动态自我建模
Richard Marques Monteiro1, Jialei Shi2, Helge Wurdemann2
1Bio-Inspired Robotics Lab, University of Cambridge, Cambridge, United Kingdom.
Frontiers in robotics and AI
|June 20, 2024
概括
本研究介绍了一种基于学习的新方法,用于在视觉空间中直接动态建模软机器人. 这种方法使各种软机器人系统能够在没有预定义结构的情况下进行全面的控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
- 控制理论 控制理论
背景情况:
- 软机器人具有复杂的非线性动力学和高度的自由度,对传统的建模和控制构成重大挑战.
- 现有的减少订单模型通常会损害控制精度,并限制软机器人的操作范围.
研究的目的:
- 开发一种基于端到端学习的方法,用于对一般机器人系统进行完全动态的建模.
- 允许在视觉空间中直接学习动态模型,绕过预定义的结构假设.
主要方法:
- 采用端到端的学习框架,从视觉观察中直接学习动态模型.
- 生成的模型具有与观察空间相匹配的维度,使复杂性适应感官系统.
- 该方法应用于用于控制器开发的软机器人操纵器.
主要成果:
- 提出的方法成功地模拟了一个软机器人操纵器,只使用90分钟的真实世界数据.
- 在控制器开发中证明适用性,实现动态控制任务,如形状控制,轨迹跟踪和避开障碍物.
- 学习的模型实现了广泛的动态控制能力.
结论:
- 本书提出了一项全面的战略,用于控制一般软机器人系统,无论它们的形状,特性或维度如何.
- 视觉空间动态建模方法为软机器人控制提供了一个强大的,不受约束的方法.
- 这些发现为更先进和多功能软机器人应用铺平了道路.
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