扩展的残留学习与一次性模仿学习,用于半结构化环境中的机器人组装
Chuang Wang1, Chupeng Su1, Baozheng Sun1
1Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China.
Frontiers in neurorobotics
|May 14, 2024
概括
本研究介绍了对象体现中心的模拟和残余增强学习 (OEC-IRRL) 方法,用于机器人组装. OEC-IRRL方法提高了学习效率,实现了更高的成功率,并减少了使用最小演示的组装时间.
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
- 计算机视觉 计算机视觉
背景情况:
- 机器人组装需要精确的操纵和协调,通常需要先进的学习技术.
- 现有的方法,如残余增强学习,在半结构化环境中面临局限性,因为它们依赖于手工设计的功能或广泛的演示.
研究的目的:
- 为机器人组装提出一个创新的对象体现中心模拟和残余增强学习 (OEC-IRRL) 方法.
- 通过尽量减少环境相互作用和利用单个演示来提高半结构化环境中的学习效率和有效性.
主要方法:
- 开发了一个以对象为中心的 (OEC) 任务表示,将视觉模型与模仿和残留学习相结合.
- 采用模仿学习与通过点运动原始体,以实现可概括的基础政策.
- 在组装过程中利用剩余强化学习来提高不确定性意识的政策精细化.
主要成果:
- 在半结构化的环境中,OEC任务代表对基础和剩余政策学习产生了积极的影响.
- 成功率提高了46%,组装时间减少了25%.
- 只需要一次演示和不到1.2小时的互动.
结论:
- OEC-IRRL方法为机器人组装任务提供了一个可行的解决方案.
- 该方法消除了对专业专业知识或定制固定装置的需求,扩大了适用性.
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