视觉力量融合课程学习,用于机器人接触丰富的组装任务
Piaopiao Jin1, Yinjie Lin2, Yaoxian Song1
1Department of Engineering Mechanics, Center for X-Mechanics, Zhejiang University, Hangzhou, China.
Frontiers in neurorobotics
|October 23, 2023
概括
这项研究引入了一种新的视觉力量课程政策学习方法,用于机器人组装. 这种方法有效地融合了视觉和力数据,使精确的操纵任务具有高概括能力.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机械工程 机械工程
背景情况:
- 与组装一样,接触丰富的机器人操纵对于制造至关重要.
- 现有的方法很难有效地融合视觉和强制传感器数据来完成这些任务.
研究的目的:
- 开发一个统一的机制,协调机器人操纵中的多式联络感知和控制.
- 为有效的传感器融合提出一个视觉强力课程政策学习方案.
主要方法:
- 开发了一个新的视觉力量课程政策学习计划.
- 该方法整合了视觉和力传感器的特征,以生成控制策略.
- 在模拟中进行了用于插入任务的实验.
主要成果:
- 提出的方法实现了精确的钉子插入与0.1毫米的空隙.
- 该系统展示了对各种初始配置和未见形状的概括性.
- 从模拟到现实实现了稳健的转移,没有微调.
结论:
- 视觉力量课程政策学习计划有效地将多式联接传感器数据用于机器人组装.
- 该方法显示了显著的有效性,概括性和sim-to-real传输能力.
- 这种方法推进了工业应用的接触丰富的机器人操纵.
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