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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (6): 11-24.doi: 10.6040/j.issn.1672-3961.0.2019.229

• 控制科学与工程——机器人专题 • 上一篇    下一篇

机器人操作技能自主认知与学习的研究现状与发展趋势

王薇1(),吴锋2,周风余3,*()   

  1. 1. 齐鲁工业大学计算机科学与技术学院, 山东 济南 250353
    2. 中国科学技术大学计算机科学与技术学院, 安徽 合肥 230026
    3. 山东大学控制科学与工程学院, 山东 济南 250061
  • 收稿日期:2019-06-13 出版日期:2019-12-20 发布日期:2019-12-17
  • 通讯作者: 周风余 E-mail:benwei85@sina.com;zhoufengyu@sdu.edu.cn
  • 作者简介:王薇(1981—)，女，山东济南人，讲师，博士，主要研究方向为机器人认知智能. E-mail: benwei85@sina.com
  • 基金资助:
    国家重点研发计划项目(2017YFB1302400);国家自然科学基金资助项目(61773242);国家自然科学基金资助项目(61802213);山东省重大科技创新工程项目(2017CXGC0926);山东省重点研发计划(公益类专项)项目(2017GGX30133)

Research status and development trend of autonomous cognition and learning of robot manipulation skills

Wei WANG1(),Feng WU2,Fengyu ZHOU3,*()   

  1. 1. School of Computer Science and Technology, Qilu University of Technology, Jinan 250353, Shandong, China
    2. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, Anhui, China
    3. School of Control and Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2019-06-13 Online:2019-12-20 Published:2019-12-17
  • Contact: Fengyu ZHOU E-mail:benwei85@sina.com;zhoufengyu@sdu.edu.cn
  • Supported by:
    国家重点研发计划项目(2017YFB1302400);国家自然科学基金资助项目(61773242);国家自然科学基金资助项目(61802213);山东省重大科技创新工程项目(2017CXGC0926);山东省重点研发计划(公益类专项)项目(2017GGX30133)

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摘要：

机器人对操作技能的自主学习是未来机器人服务人类社会所需具备的重要技能之一,也是机器人研究领域的热点问题之一。对目前机器人操作技能学习的主流模式、方式、算法以及不同方法的优缺点做了全面综述,归纳了在未来知识共享模式下个体机器人实现操作技能的自主学习所面临的挑战和亟待解决的关键问题,并介绍了一种将机器人个体学习模式与共享学习模式有机结合提升机器人操作技能的自主认知与学习的潜在解决方案。

关键词: 云机器人, 共享知识型机器人, 操作技能, 自主学习, 自主认知

Abstract:

Autonomous cognition and learning of manipulation skills, being one of the most important skills for robots, has been one of the hot issues in the field of robotics research. Combining with the authors' work in the field of robotics, this paper's focus is placed on giving a comprehensive overview of the mainstream modes, methods, algorithms, as well as advantages and disadvantages of different methods in terms of robots' manipulation skill learning. It concludes the challenges faced by autonomous learning and the key issues that need to be addressed for the individual cloud robots learning manipulation skills in the knowledge sharing mode. At the end, a potential solution for the above issues is given, and that is to integrate individual learning mode and shared learning model for the purpose of enhancing autonomous cognition and learning ability for robots.

Key words: cloud robot, knowledge-sharing robot, manipulation skills, autonomous learning, autonomous cognition

中图分类号: 

  • TP242

表1

操作技能表达形式举例"

代表项目 年代 国家 技能表示形式 学习方式 基本技能单元 技能单元性质 是否渐进式发展 跨机器人平台 跨工作种类 跨工作环境
Task Transfer 2007 美国 数值表示 发展式学习 数值 离散 是 否 是 否
Jean 2009 美国 符号表示 发展式学习 模板 离散 否 否 否 否
QLAP 2009 美国 定性表示 发展式学习 技能选项 离散 是 否 是 否
RoboEarth 2010 欧盟 符号表示 一次性获取 本体与语义描述 连续 否 是 是 否
CST 2013 美国 符号表示 从自身经验学习+发展式学习 技能抽象 连续 是 否 是 否

表2

共享操作类技能和知识的代表项目基本情况"

项目名称 年代 研究单位 国家(地区) 技能描述方法 技能自主发展
RoboEarth 2009 TUM 欧盟 本体、语义网络 否
RoboBrain 2013 加州伯克利分校 美国 常识维基百科 否
PR2叠毛巾 2015 康奈尔大学、斯坦福大学 美国 Twitter+深度神经网络 否
万物交流 2016 布朗大学 美国 ROS节点 否
Google Mind 2016 谷歌公司 美国 指令 否
C-LEARN 2017 麻省理工学院 美国 具有几何约束的规则数据库 否

表3

机器人技能学习与发展的主要学习方法比较"

技能学习方法 个体学习 社交学习 特点 主要挑战 主要技术
从演示中学习 支持 支持(非)面对面HRI、面对面的RRI 简单直接无需编程无需专家 缺少大量、高质量的演示数据 监督学习、强化学习、深度强化学习
发展式学习 支持 支持面对面HRI、RRI 模拟婴儿心智发育过程、渐进式技能增长 初始知识的数量和形式、可持续发展的主导程序设计、技能表示形式 强化学习、深度强化学习、DCO、DDCO
类脑学习 支持 不支持 模拟成人大脑、个案学习 视觉感知、与人沟通、大脑思考、自适应能力 基于CNN的深度学习、强化学习、基于生成模型的贝叶斯学习
共享知识学习 不支持 支持非面对面HRI、RRI 海量数据不受时空限制、一次性技能获取 数据的跨平台共享、共享数据的跨平台迁移学习 强化学习、深度强化学习、云计算

图1

个体学习模式与共享知识学习模式融合(SRDL[20], CST技能抽象[27])"

表4

共享技能迁移学习的四个级别"

级别 工作 机器人平台 所需主要技术
第一级 已知 相同 自学迁移学习[53]
第二级 已知 不同 身体对应问题[4, 74]、自学迁移学习
第三级 未知 相同 多任务迁移学习[53]、身体对应问题
第四级 未知 不同 多任务迁移学习、身体对应问题、知识空白填补[75]

图2

雾机器人架构[80]"

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[1] 田国会, 许亚雄. 云机器人:概念、架构与关键技术研究综述[J]. 山东大学学报(工学版), 2014, 44(6): 47-54.
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