人-机器人技能传递研究进展

曾超; 杨辰光; 李强; 戴诗陆

doi:10.16383/j.aas.c180397

人-机器人技能传递研究进展

doi: 10.16383/j.aas.c180397

曾超^1,,
杨辰光^1, ,,
李强^2,,
戴诗陆^1,

1.
华南理工大学自动化科学与工程学院自主系统与网络控制教育部重点实验室广州 510641 中国
2.
德国比勒费尔德大学认知交互技术研究中心比勒费尔德 33619 德国

基金项目:

国家自然科学基金 61811530281

中央高校基本科研业务费专项资金，机器人技术与系统国家重点实验室开放研究项目 SKLRS-2017-KF-13

广州市科技计划项目 201607010006

广州市科技计划项目 201604016082

国家自然科学基金 61473120

国家自然科学基金 61473121

详细信息

作者简介:
曾超  华南理工大学自动化科学与工程学院博士研究生.2016年获得上海大学工学硕士学位.主要研究方向为人-机器人物理交互, 人机技能传递, 机械臂自适应控制.E-mail:mjzengchao@163.com

李强  德国比勒费尔德大学认知交互技术研究中心高级研究员.2010年获得中国科学院沈阳自动化研究所博士学位.主要研究方向为多指机械手灵巧操作, 视觉-触觉-力伺服控制, 基于多模态反馈的机器人自校准, 移动机器人导航与控制, 基于滤波理论的参数估计与优化.E-mail:qli@techfak.uni-bielefeld.de

戴诗陆  华南理工大学自动化科学与工程学院教授.2010年获得东北大学博士学位.主要研究方向为自适应与学习控制, 分布式协同控制系统.E-mail:audaisl@scut.edu.cn

通讯作者:
杨辰光华南理工大学自动化科学与工程学院教授.2010年获得新加坡国立大学博士学位.主要研究方向为机器人与自动化, 先进控制方法, 人机交互与人机协作.本文通信作者. E-mail:auyangcg@gmail.com

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出版历程
- 收稿日期: 2018-06-04
- 录用日期: 2018-10-09
- 刊出日期: 2019-10-20

Research Progress on Human-robot Skill Transfer

ZENG Chao^1
,,
YANG Chen-Guang^{1
, ,},
LI Qiang^2
,,
DAI Shi-Lu^1
,

1.
Key Laboratory of Autonomous Systems and Networked Control, School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
2.
Cluster of Excellence Cognitive Interaction Technology, Bielefeld University, Bielefeld 33619, Germany

Funds:

National Natural Science Foundation of China 61811530281

Fundamental Research Funds for the Central Universities, State Key Laboratory of Robotics and System (HIT) SKLRS-2017-KF-13

Science and Technology Planning Project of Guangzhou 201607010006

Science and Technology Planning Project of Guangzhou 201604016082

National Natural Science Foundation of China 61473120

National Natural Science Foundation of China 61473121

More Information

Author Bio:
Ph. D. candidate at the College of Automation Science and Engineering, South China University of Technology. He received his master degree from Shanghai University in 2016. His research interest covers human-robot physical interaction, human-robot skill transfer, and adaptive control for robotic arms

Senior researcher at Cluster Excellence Cognitive Interaction Technology, Bielefeld University, Germany. He received his Ph. D. degree from Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS) in 2010. His research interest covers dexterous manipulation of multifingered robot hand, visuo-tactile-force servo control, multi-modality feedback based robot selfcalibration, mobile robot navigation and control and optimal parameter estimation based on filter theory

Professor at the College of Automation Science and Engineering, South China University of Technology. He received his Ph. D. degree from the Northeastern University in 2010. His research interest covers adaptive and learning control, distributed cooperative control systems

Corresponding author: YANG Chen-Guang Professor at the College of Automation Science and Engineering, South China University of Technology. He received his Ph. D. degree from the National University of Singapore, Singapore in 2010. His research interest covers robotics and automation, advanced control methods, human-robot interaction and collaboration. Corresponding author of this paper

摘要

摘要: 人-机器人技能传递（Human-robot skill transfer，HRST）是指人将操作技能传授给机械臂使得机器人具备类人化的作业能力，以达到高效示教编程的目的.相对于传统的机器人编程技术，人机技能传递具有高效率、低成本、不依赖机器本体平台等显著优点，是人-信息-机器人融合系统（Human-cyber-robot-systems，HCRS）中重要环节之一，应当给予足够的重视.本文首先介绍了人机技能传递技术的研究背景，接着简述了该技术在人机接口、建模、仿生自适应控制等方面的发展现状，并对未来的研究方向做出了展望.
- 技能传递 /
- 运动建模 /
- 仿人控制 /
- 人机交互
Abstract: Human-robot skill transfer (HRST) is a general method to transfer human's skills to robots in order that robots can perform tasks in a human-like way. With this method, robots can be programmed efficiently. Comparing with conventional program methods, HRST has a number of significant advantages such as high efficiency, low cost and independent of robotic platforms. It is one of the most important parts in human-cyber-robotic systems (HCRS), which should be paid much attention to. In this paper, we firstly introduce the background of HRST, then introduce human robot interface, modelling and control domain. Finally we show some potential research lines in future.
- Human-robot skill transfer (HRST) /
- motion modelling /
- bio-inspired control /
- human-robot interaction
注释:

1) 本文责任编委穆朝絮

HTML全文

图 1 人机技能传递一般过程框图

Fig. 1 The procedure of human-robot skill transfer

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图 2 基于视觉方式的技能传递^[19]

Fig. 2 Vision-based human-robot skill transfer interface^[19]

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图 3 基于遥操作方式的人机技能传递^[16]

Fig. 3 Teleoperation-based human-robot skill transfer^[16]

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图 4 基于物理交互方式的人机技能示教^[24]

Fig. 4 Physical interaction for human-robot skill transfer^[24]

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图 5 双臂示教方式^[25]

Fig. 5 Demonstration based on dual arm teaching^[25]

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图 6 DMP模型表征运动轨迹示例

Fig. 6 Examples of DMP modelling: converging to goals

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图 7 基于DMP模型的双臂技能示教学习^[39]

Fig. 7 DMP-based robot bimanual skill learning by demonstration^[39]

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图 8 GMM, HMM, HSMM三种模型关系图^[65]

Fig. 8 Graphical representation of the GMM, HMM and HSMM models^[65]

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图 9 基于EMG信号的人机变刚度传递系统^[91]

Fig. 9 The EMG-based human-robot stiffness transfer system^[91]

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图 10 基于变刚度控制的人机协作^[94]

Fig. 10 Human-robot collaboration based on variable stiffness control^[94]

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表 1 HRST与传统方式的比较

Table 1 Comparation between HRST and the conventional methods

	传统编程方式	人机技能传递
交互方式	人机隔离	共融交互
交互感受	编程不直观	自然、直观
编程人员	需要专业工程师	不依赖专业人员
机器人平台	针对具体机器人平台	技能不受限于特定平台
工作空间	受限于作业环境	受限程度小
任务情况	任务具体、固定	适应于不同任务, 可优化
编程效率	效率低下, 耗时多	效率较高, 便于重配置

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表 2 DMP、GMM、HMM模型特点总结

Table 2 The summary of DMP、GMM、HMM models

模型	常见变种	基本特点	技能示例
DMP	-	模型简单; 拓展性好; 学习单次示教; 计算效率高$^{1}$.	Tennis swings^[75]
	Bio-inpisred DMP	可以克服跨过零点问题, 可在线动态避障.	Pick-and-place^[34]
	PDMP	适用高维$^{2}$、连续系统; 对多种运动灵活表达.	Walking^[36]
	GDMP	可实现多种控制策略, 起到意图预测等作用.	Grasping^[37]
	AL-DMP	空间与时间信息分别表示, 更好地表达运动速度.	Reaching positions^[39]
	SDMP	可从多次差异较大的示教结果中学习技能特征$^{3}$.	Table tennis^[41]
	ProMP$^{4}$	对运动原语概率化表示; 可有机混合不同运动原语.	Robot hockey^[76]
	Coupling DMP	耦合双臂运动信息, 适宜双臂、协作操作任务.	Bimanual tasks^[35]
	DMP-based RL	通过强化学习方法对DMP轨迹优化.	Ball-in-a-cup^[43]
GMM	-	可表达不同维度的关联信息; 可表征多次示教; 计算效率相对低.	Gripper assembly^[77]
	ILO-GMM	局部耦合运动信息; 增量学习运动技能.	Moving^[54]
	TP-GMM	耦合任务参数到模型中; 对参数化轨迹在线调节.	Rolling out a pizza^[56]
	TP-GMM on RM$^{5}$	用黎曼流形表示GMM, 有效表达末端位姿分布信息.	Bimanual pouring^[59]
HMM	-	相比GMM对运动的信息表达能力更强; 计算效率相比较低.	Ball-in-box^[78]
	HMM-GMR	用GMR做回归模型, 可在线生成运动控制命令; 鲁棒性好.	Feeding^[67]
	HMM-LPV	保证每个子状态的稳定性, 适宜复杂任务建模.	Reach-Peel-retractg^[69]
	HSMM	可表达状态驻留时间, 相比HMM抗外界干扰能力强.	Button pushing^[12]
	ADHSMM	自适应调节状态驻留时间, 对时间信息表达能力更强.	Pouring^[73]
¹ 计算效率高是指离线下模型学习时间短, 这里不包括基于DMP的强化学习算法. ² 指对多个自由度个数, 如对7-DOF的机械臂同时学习位置与速度, 则维度为14. ³ 指多次示教的轨迹重合度小, 难于对齐, 如打乒乓球时的运动轨迹. ⁴ 概率化运动原语(Probabilistic movement primitives, ProMP). ⁵ 指黎曼流形(Riemannian manifolds, RM).

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