在模块化机器人上并行学习多种技能的无模型方法
Fuda van Diggelen1, Nicolas Cambier2, Eliseo Ferrante2
1Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Noord-Holland, the Netherlands. fuda.van.diggelen@vu.nl.
Nature communications
|July 24, 2024
概括
这项研究介绍了一种新的中央模式生成器 (CPG) 方法,用于腿类机器人,可以在现实世界中直接快速学习机器人运动技能,而没有先前的机器人特定动态. 机器人很快就获得了基本的运动能力,克服了模拟到现实转移的挑战.
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
- 人工智能的人工智能
背景情况:
- 双腿机器人擅长在非结构化环境中工作,但需要专门的控制.
- 模拟与现实之间的差距阻碍了模拟机器人控制器的直接应用.
- 快速,无模型的真实世界学习方法对于机器人部署至关重要.
研究的目的:
- 介绍一种通用方法,用于在腿类机器人中获得基本的机动技能.
- 为应对将控制器从模拟转移到现实世界的挑战.
- 为了实现快速,并行学习的机动与最小的现实世界的试验.
主要方法:
- 使用中央模式发生器 (CPG) 进行机动控制.
- 专注于优化初始状态,而不是在CPG模型中的连接权重.
- 使用数学分析来支持控制器模型的新性.
- 在六种不同的机器人形态学中进行实证验证.
主要成果:
- 拟议的方法使机器人能够快速学习基本运动技能.
- 在现实世界的实验中,学习时间不到15分钟.
- 该方法在各种机器人设计中显示出有效性.
- 在一个有针对性的运动任务中成功展示学到的技能.
结论:
- 基于CPG的方法有效地弥合了模拟与真实之间的差距,用于腿类机器人机动.
- 优化初始状态为CPG的重量优化提供了一个可行的替代方案.
- 该技术在真实机器人中促进了快速,可适应和无模型的技能获取.
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