基于阻抗的多模式驱动Sim2Real转移学习用于机器人多重插孔组件
IEEE transactions on cybernetics
|September 15, 2023
概括
这项研究引入了一种新的工业Metaverse方法,用于机器人多重入孔组装,增强智能制造能力. 该方法有效地将强化学习政策从模拟转移到现实世界的应用.
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
- 智能制造 智能制造是一种智能制造.
背景情况:
- 机器人刚性接触丰富的操纵对于智能制造至关重要.
- 强化学习 (RL) 已经改善了单个入洞组件,但由于复杂的约束,它在多个入洞任务中扎.
- 现有的多重钉入孔组装解决方案缺乏适用于现实世界的工业部署的灵活性.
研究的目的:
- 使用工业Metaverse设计一种新且具有挑战性的多重钉入孔组装设置.
- 为复杂的机器人组装任务开发一个强大的解决方案方案.
- 允许灵活有效地将学到的政策转移到实际的工业场景中.
主要方法:
- 利用工业元宇宙进行了一种新的多重钉入孔组件设置.
- 集成的多模态传感输入 (视觉,自身感知,力/扭矩) 用于紧的表示学习.
- 在模拟中使用强化学习,使用域随机化和阻抗控制进行模拟到真实传输.
主要成果:
- 在现实场景中成功展示了有效的多重钉入孔组件.
- 从模拟到现实实现了政策转移,没有额外的现实世界探索.
- 展示了不同物体形状的概括能力.
结论:
- 拟议的基于工业元宇宙的方法有效地解决了机器人多重入孔组装方面的挑战.
- 多模式学习和模拟到现实转移技术提高了样本效率和现实世界的适用性.
- 该解决方案为智能制造应用提供了一种灵活而强大的方法.
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