学习软米利机器人多式联动机动与SIM到真实转移
Sinan Ozgun Demir1,2, Mehmet Efe Tiryaki1, Alp Can Karacakol1
1Physical Intelligence Department, Max Planck Institute for Intelligent Systems, 70569, Stuttgart, Germany.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|June 18, 2024
概括
本研究为磁性软米利机器人提供了一个基于学习的框架,使其能够实现自适应式多式联运机动. 该系统使用sim-to-real转移,以最少的成本高效地适应新环境.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 磁性软型千米机器人提供无线,多式联动的无线移动,用于最少的侵入性医疗应用.
- 目前的机动适应依赖于手动信号调,限制了效率和适应性.
研究的目的:
- 开发一种基于学习的框架,用于磁性软型毫米机器人的自适应多式交通.
- 为了使自主适应多样化和未知的环境使用模拟到现实转移.
主要方法:
- 创建了一个数据驱动的模拟环境,用于学习磁性执行信号.
- 贝叶斯优化和高斯过程促进了机动策略的模拟到真实转移.
- 库尔巴克-莱布勒分歧用于自动化域名识别和适应.
主要成果:
- 该框架在模拟中成功学习了用于米利机器人运动的周期性磁性启动信号.
- 学习的策略被有效地部署到现实世界的机器人中.
- 证明了对未知的环境的自动适应,展示了快速和持续的环境反应.
结论:
- 拟议的基于学习的框架提高了磁性软米利机器人的适应性和效率.
- 模拟到真实转移和概率方法使在不断变化的环境中实现了稳健的移动.
- 这种方法最大限度地降低了实验成本,并探索了新的执行解决方案.
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