協作機器人智能控制與人機交互研究綜述

黃海豐; 劉培森; 李擎; 于欣波

doi:10.13374/j.issn2095-9389.2021.08.31.001

協作機器人智能控制與人機交互研究綜述

doi: 10.13374/j.issn2095-9389.2021.08.31.001

黃海豐^{1, 2},
劉培森^{1, 2},
李擎^1, ,,
于欣波^{2, 3}

1.
北京科技大學自動化學院，北京 100083
2.
北京科技大學人工智能研究院，北京 100083
3.
北京科技大學順德研究生院，佛山 528399

基金項目: 國家自然科學基金資助項目（62073031, 62061160371, 62003032）；“人工智能科學與工程”北京市高精尖學科資助項目；北京科技大學青年教師學科交叉研究項目（FRF-IDRY-20-019）

詳細信息

通訊作者:
E-mail: liqing@ies.ustb.edu.cn

中圖分類號: TP242.6
計量
- 文章訪問數: 1095
- HTML全文瀏覽量: 2490
- PDF下載量: 443
- 被引次數: 0
出版歷程
- 收稿日期: 2021-08-31
- 網絡出版日期: 2021-10-26
- 刊出日期: 2022-04-02

Review: Intelligent control and human-robot interaction for collaborative robots

HUANG Hai-feng^{1, 2},
LIU Pei-sen^{1, 2},
LI Qing^{1
, ,},
YU Xin-bo^{2, 3}

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China
3.
Shunde Graduate School, University of Science and Technology Beijing, Foshan 528399, China

More Information

Corresponding author: E-mail: liqing@ies.ustb.edu.cn

摘要

摘要: 協作機器人是一類能夠在共享空間中與人類交互或在人類附近安全工作的新型工業機器人，由于其輕質、安全的特點，在柔性制造、社會服務、醫療健康、防災抗疫等多個領域展現出了良好的應用前景，受到工業界和學術界的廣泛關注，成為當前機器人領域的研究熱點之一。協作機器人需要具備良好的控制性能確保與人交互的安全性，集成多種傳感器感知外部環境并應用智能控制理論與方法來確保高效的協作行為。在我國，人機協作已列入《智能制造2025》和《新一代人工智能發展規劃》重點支持研究計劃。本文主要介紹了國內外幾款常見的協作機器人，機器人基于感知信息的控制、高精度跟蹤控制、交互控制等智能控制方法，并圍繞機器人與人執行協作任務的高效性，對機器人的人類意圖估計和技能學習方法進行了討論。最終對協作機器人未來的發展方向進行了展望。
- 協作機器人 /
- 智能控制 /
- 人機交互 /
- 機器人技能學習 /
- 人類意圖估計
Abstract: The aggravating trend of an aging population impacts industrial production and social services. Robots are expected to be able to work not only in highly structured manufacturing environments but also in human-inhabited environments, and hence, need to have more sophisticated cognitive abilities. They have to be able to operate safely and efficiently in unstructured, populated environments and achieve high-level collaboration and communication with humans. Collaborative robots, also referred to as cobots, are a new class of industrial robots that can interact with humans in shared spaces or work safely in the vicinity of humans. Collaborative robots are generally lightweight and edge-rounded with multiple degrees of freedom. Besides, multiple sensors must be integrated and limitations of speed and force must be set to ensure their behavior safety. Collaborative robots have shown good application prospects in many fields, such as flexible manufacturing, social services, medical care, disaster prevention, and antiepidemic. They have received wide attention in the industry and academia. Collaborative robots require the integration of multimodal sensory information and intelligent control methods to ensure efficient collaborative behavior. Human-robot collaboration (HRC) considers key issues attached to how safe and efficient collaboration between cobots and humans can be achieved, involving robotics, cognitive sciences, machine learning, artificial intelligence, philosophy, and others. HRC has been included in the key support research programs such as Smart Manufacturing 2025 and the Development Plan of New Generation Artificial Intelligence, recently becoming an important research direction in the field of intelligent robotics with a wide range of applications. This paper introduces several domestic and foreign collaborative robots and intelligent control methods of collaborative robots, including control methods based on perception information, high accuracy tracking control methods, and interaction control methods. It also discusses human intention estimation and robot skill learning methods for efficient human-robot collaboration. Finally, future directions of collaborative robots are explored.
- collaborative robot /
- intelligent control /
- human-robot interaction /
- robot skill learning /
- human intention estimation

HTML全文

表 1 幾款國外協作機器人

Table 1. Introduction to collaborative robots from foreign manufacturers

Model	Manufacturers	Features
UR5	Universal Robots	Single-arm, 6DoF, repeatability accuracy ±0.1 mm, payload 5 kg
UR5e	Universal Robots	Single-arm, 6DoF, repeatability accuracy ±0.1 mm, payload 5 kg, force control sensor integrated
LBR iiwa	KUKA	Single-arm, 7DoF, repeatability accuracy ±0.1 mm, payload 7 kg, torque sensor integrated
CR-35iA	FANUC	Single-arm, 6DoF, repeatability accuracy ±0.08 mm, payload 35 kg
Jaco2 6Dof	KINOVA	Single-arm, 6DoF, net mass 5.2 kg, payload 1.3 kg, joystick operation
Gen3	KINOVA	Single-arm, 7DoF, net mass 8.2 kg, payload 4 kg
Panda	Franka Emika	Single-arm, 7DoF, repeatability accuracy ±0.1 mm, payload 3 kg, accurate collision detection

下載: 導出CSV

表 2 幾款國內協作機器人

Table 2. Introduction to collaborative robots from domestic manufacturers

Model	Manufacturers	Features
SCR5	SIASUN	Single-arm, 7DoF, repeatability accuracy ±0.02 mm, payload 5 kg
AUBO i5	AUBO	Single-arm, 6DoF, repeatability accuracy ±0.02 mm, payload 5 kg
xMate7	ROKAE	Single-arm, 7DoF, repeatability accuracy ±0.03 mm, payload 7 kg, sensitive force perception
CS66	ELITE ROBOT	Single-arm, 6DoF, repeatability accuracy ±0.03 mm, payload 7 kg
AI 3	JAKA	Single-arm, 6DoF, repeatability accuracy ±0.02 mm, payload 3 kg, vision integrated
myCobot	Elephant Robotics	Single-arm, 6DoF, repeatability accuracy ±0.03 mm, payload 250 g, net mass 850 g

下載: 導出CSV

表 3 三種常見的示教學習方法對比

Table 3. Comparison of three common demonstration methods

Method	Difficulty for the teacher	Computational complexity	Advantages	Disadvantages
Kinesthetic teaching	Low	Low	Easy to demonstrate	Cannot execute fast movements, can only demonstrate one limb at a time
Teleoperation	Medium	Medium	Remote demonstrate	Time delay
Observational learning	Lowest	High	Easy to demonstrate, works for bimanual tasks or even whole-body motion	Correspondence difficulty caused by the different embodiment

下載: 導出CSV

久色视频

參考文獻(111)

[1]	He W, Li Z J, Chen C L P. A survey of human-centered intelligent robots: Issues and challenges. IEEE/CAA J Autom Sin, 2017, 4(4): 602 doi: 10.1109/JAS.2017.7510604
[2]	Baraglia J, Cakmak M, Nagai Y, et al. Efficient human-robot collaboration: When should a robot take initiative? Int J Robotics Res, 2017, 36(5-7): 563
[3]	Ajoudani A, Zanchettin A M, Ivaldi S, et al. Progress and prospects of the human-robot collaboration. Auton Robots, 2018, 42(5): 957 doi: 10.1007/s10514-017-9677-2
[4]	Dhome M, Richetin M, Lapreste J T, et al. Determination of the attitude of 3D objects from a single perspective view. IEEE Trans Pattern Anal Mach Intell, 1989, 11(12): 1265 doi: 10.1109/34.41365
[5]	Dementhon D F, Davis L S. Model-based object pose in 25 lines of code. Int J Comput Vis, 1995, 15(1-2): 123 doi: 10.1007/BF01450852
[6]	Larouche B P, Zhu Z H. Autonomous robotic capture of non-cooperative target using visual servoing and motion predictive control. Auton Robots, 2014, 37(2): 157 doi: 10.1007/s10514-014-9383-2
[7]	Li S C, Zhang J, Zhang H, et al. Object pose estimation method for robot grasping process. Transducer Microsyst Technol, 2019, 38(7): 32 李樹春, 張靜, 張華, 等. 面向機器人抓取過程中目標位姿估計方法. 傳感器與微系統, 2019, 38(7):32
[8]	Li B Q, Fang Y C, Zhang X B. 2D trifocal tensor based visual servo regulation of nonholonomic mobile robots. Acta Autom Sin, 2014, 40(12): 2706 李寶全, 方勇純, 張雪波. 基于2D三焦點張量的移動機器人視覺伺服鎮定控制. 自動化學報, 2014, 40(12):2706
[9]	Ke F, Li Z J, Xiao H Z, et al. Visual servoing of constrained mobile robots based on model predictive control. IEEE Trans Syst Man Cybern:Syst, 2017, 47(7): 1428 doi: 10.1109/TSMC.2016.2616486
[10]	Zhang X T, Fang Y C, Zhang X B, et al. Dynamic image-based output feedback control for visual servoing of multirotors. IEEE Trans Ind Inform, 2020, 16(12): 7624 doi: 10.1109/TII.2020.2974485
[11]	Malis E, Chaumette F. 2 1/2 D visual servoing with respect to unknown objects through a new estimation scheme of camera displacement. Int J Comput Vis, 2000, 37(1): 79 doi: 10.1023/A:1008181530296
[12]	He Z X, Wu C R, Zhang S Y, et al. Moment-based 2.5-D visual servoing for textureless planar part grasping. IEEE Trans Ind Electron, 2019, 66(10): 7821
[13]	Prats M, Sanz P J, del Pobil A P. Vision-tactile-force integration and robot physical interaction // 2009 IEEE International Conference on Robotics and Automation. Kobe, 2009: 3975
[14]	Ilonen J, Bohg J, Kyrki V. Fusing visual and tactile sensing for 3-D object reconstruction while grasping // 2013 IEEE International Conference on Robotics and Automation. Karlsruhe, 2013: 3547
[15]	Alt N, Steinbach E. Navigation and manipulation planning using a visuo-haptic sensor on a mobile platform. IEEE Trans Instrum Meas, 2014, 63(11): 2570 doi: 10.1109/TIM.2014.2315734
[16]	Wang S X, Wu J J, Sun X Y, et al. 3D shape perception from monocular vision, touch, and shape priors // 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, 2018: 1606
[17]	Li J H, Dong S Y, Adelson E. Slip detection with combined tactile and visual information // 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane, 2018: 7772
[18]	Zheng W D, Liu H P, Sun F C. Lifelong visual-tactile cross-modal learning for robotic material perception. IEEE Trans Neural Netw Learn Syst, 2021, 32(3): 1192 doi: 10.1109/TNNLS.2020.2980892
[19]	Yu X B, He W, Li Q, et al. Human-robot co-carrying using visual and force sensing. IEEE Trans Ind Electron, 2021, 68(9): 8657 doi: 10.1109/TIE.2020.3016271
[20]	Karason S P, Annaswamy A M. Adaptive control in the presence of input constraints. IEEE Trans Autom Control, 1994, 39(11): 2325 doi: 10.1109/9.333787
[21]	Gao W Z, Selmic R R. Neural network control of a class of nonlinear systems with actuator saturation. IEEE Trans Neural Netw, 2006, 17(1): 147 doi: 10.1109/TNN.2005.863416
[22]	Chen M, Ge S S, Ren B B. Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints. Automatica, 2011, 47(3): 452 doi: 10.1016/j.automatica.2011.01.025
[23]	Chen M, Ge S S, How B V E. Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities. IEEE Trans Neural Netw, 2010, 21(5): 796 doi: 10.1109/TNN.2010.2042611
[24]	He W, Li Z, Dong Y, et al. Design and adaptive control for an upper limb robotic exoskeleton in presence of input saturation. IEEE Trans Neural Netw Learn Syst, 2018, 30(1): 97
[25]	Zheng W H, Jia Y M. Adaptive tracking control for omnidirectional mobile robots with full-state constraints and input saturation. Chin J Eng, 2019, 41(9): 1176 鄭文昊, 賈英民. 具有狀態約束與輸入飽和的全向移動機器人自適應跟蹤控制. 工程科學學報, 2019, 41(9):1176
[26]	Spong M W. Modeling and control of elastic joint robots. J Dyn Syst Meas Control, 1987, 109(4): 310 doi: 10.1115/1.3143860
[27]	Chen G R, Desages A, Julian P. Trajectory tracking and robust stability for a class of time-delayed flexible-joint robotic manipulators. Int J Control, 1997, 68(2): 259 doi: 10.1080/002071797223596
[28]	Chang Y C, Wu M F. Robust tracking control for a class of flexible-joint time-delay robots using only position measurements. Int J Syst Sci, 2016, 47(14): 3336 doi: 10.1080/00207721.2015.1129677
[29]	Li H Y, Zhao S Y, He W, et al. Adaptive finite-time tracking control of full state constrained nonlinear systems with dead-zone. Automatica, 2019, 100: 99 doi: 10.1016/j.automatica.2018.10.030
[30]	Zhang Z K, Duan G R, Hou M Z. Robust adaptive dynamic surface control of uncertain non-linear systems with output constraints. IET Control Theory Appl, 2017, 11(1): 110 doi: 10.1049/iet-cta.2016.0456
[31]	Kostarigka A K, Doulgeri Z, Rovithakis G A. Prescribed performance tracking for flexible joint robots with unknown dynamics and variable elasticity. Automatica, 2013, 49(5): 1137 doi: 10.1016/j.automatica.2013.01.042
[32]	Guo T, Wu X W. Backstepping control for output-constrained nonlinear systems based on nonlinear mapping. Neural Comput Appl, 2014, 25(7-8): 1665 doi: 10.1007/s00521-014-1650-9
[33]	Meng W C, Yang Q M, Si J, et al. Adaptive neural control of a class of output-constrained nonaffine systems. IEEE Trans Cybern, 2016, 46(1): 85 doi: 10.1109/TCYB.2015.2394797
[34]	Wang M, Yang A L. Dynamic learning from adaptive neural control of robot manipulators with prescribed performance. IEEE Trans Syst Man Cybern:Syst, 2017, 47(8): 2244 doi: 10.1109/TSMC.2016.2645942
[35]	Wang M, Wang C, Shi P, et al. Dynamic learning from neural control for strict-feedback systems with guaranteed predefined performance. IEEE Trans Neural Netw Learn Syst, 2016, 27(12): 2564 doi: 10.1109/TNNLS.2015.2496622
[36]	Tee K P, Ge S S, Tay E H. Barrier Lyapunov Functions for the control of output-constrained nonlinear systems. Automatica, 2009, 45(4): 918 doi: 10.1016/j.automatica.2008.11.017
[37]	Liu Y J, Tong S C, Chen C L P, et al. Adaptive NN control using integral barrier Lyapunov functionals for uncertain nonlinear block-triangular constraint systems. IEEE Trans Cybern, 2017, 47(11): 3747 doi: 10.1109/TCYB.2016.2581173
[38]	He W, Chen Y H, Yin Z. Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans Cybern, 2016, 46(3): 620 doi: 10.1109/TCYB.2015.2411285
[39]	Liu Y, Chen X B, Mei Y F, et al. Observer-based boundary control for an asymmetric output-constrained flexible robotic manipulator. Sci China Inf Sci, 2021, 65(3): 1
[40]	Atkeson C G, An C H, Hollerbach J M. Estimation of inertial parameters of manipulator loads and links. Int J Robotics Res, 1986, 5(3): 101 doi: 10.1177/027836498600500306
[41]	Xu Z, Zhang G, Wang H M, et al. Error compensation of collaborative robot dynamics based on deep recurrent neural network. Chin J Eng, 2021, 43(7): 995 徐征, 張弓, 汪火明, 等. 基于深度循環神經網絡的協作機器人動力學誤差補償. 工程科學學報, 2021, 43(7):995
[42]	Han H G, Qiao J F, Bo Y C. On structure design for RBF neural network based on information strength. Acta Autom Sin, 2012, 38(7): 1083 doi: 10.3724/SP.J.1004.2012.01083
[43]	Narendra K S, Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw, 1990, 1(1): 4 doi: 10.1109/72.80202
[44]	Yang C G, Jiang Y M, Li Z J, et al. Neural control of bimanual robots with guaranteed global stability and motion precision. IEEE Trans Ind Inform, 2017, 13(3): 1162 doi: 10.1109/TII.2016.2612646
[45]	He W, Dong Y T, Sun C Y. Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Trans Syst Man Cybern:Syst, 2016, 46(3): 334 doi: 10.1109/TSMC.2015.2429555
[46]	Ding L, Li S, Gao H B, et al. Adaptive partial reinforcement learning neural network-based tracking control for wheeled mobile robotic systems. IEEE Trans Syst Man Cybern:Syst, 2020, 50(7): 2512 doi: 10.1109/TSMC.2018.2819191
[47]	Huang P F, Wang D K, Meng Z J, et al. Impact dynamic modeling and adaptive target capturing control for tethered space robots with uncertainties. IEEE/ASME Trans Mechatron, 2016, 21(5): 2260 doi: 10.1109/TMECH.2016.2569466
[48]	He W, Dong Y T. Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Trans Neural Netw Learn Syst, 2018, 29(4): 1174 doi: 10.1109/TNNLS.2017.2665581
[49]	Liu C X, Wen G L, Zhao Z J, et al. Neural-network-based sliding-mode control of an uncertain robot using dynamic model approximated switching gain. IEEE Trans Cybern, 2021, 51(5): 2339 doi: 10.1109/TCYB.2020.2978003
[50]	Dong Y T, Ren B B. UDE-based variable impedance control of uncertain robot systems. IEEE Trans Syst Man Cybern:Syst, 2019, 49(12): 2487 doi: 10.1109/TSMC.2017.2767566
[51]	Sun L, Zheng Z W. Finite-time sliding mode trajectory tracking control of uncertain mechanical systems. Asian J Control, 2017, 19(1): 399 doi: 10.1002/asjc.1377
[52]	Zhang Y H, Sun J, Liang H J, et al. Event-triggered adaptive tracking control for multiagent systems with unknown disturbances. IEEE Trans Cybern, 2020, 50(3): 890 doi: 10.1109/TCYB.2018.2869084
[53]	Sun C Y, He W, Hong J. Neural network control of a flexible robotic manipulator using the lumped spring-mass model. IEEE Trans Syst Man Cybern:Syst, 2017, 47(8): 1863 doi: 10.1109/TSMC.2016.2562506
[54]	Kim M J, Beck F, Ott C, et al. Model-free friction observers for flexible joint robots with torque measurements. IEEE Trans Robotics, 2019, 35(6): 1508 doi: 10.1109/TRO.2019.2926496
[55]	Chen J H, Qiao H. Muscle-synergies-based neuromuscular control for motion learning and generalization of a musculoskeletal system. IEEE Trans Syst Man Cybern:Syst, 2021, 51(6): 3993 doi: 10.1109/TSMC.2020.2966818
[56]	Na J, Zhang C, Wang X, et al. Unknown system dynamics estimator for prescribed performance control of robotic systems. Control Decis, 2021, 36(5): 1040 那靖, 張超, 王嫻, 等. 基于未知系統動態估計的機器人預設性能控制. 控制與決策, 2021, 36(5):1040
[57]	Yuan M X, Chen Z, Yao B, et al. Fast and accurate motion tracking of a linear motor system under kinematic and dynamic constraints: An integrated planning and control approach. IEEE Trans Control Syst Technol, 2021, 29(2): 804 doi: 10.1109/TCST.2019.2955658
[58]	Lozano R, Brogliato B. Adaptive hybrid force-position control for redundant manipulators // 29th IEEE Conference on Decision and Control. Honolulu, 1990: 1949
[59]	Hogan N. Impedance Control: An Approach to Manipulation // 1984 American Control Conference. San Diego, 1984: 304
[60]	Jung S, Hsia T C, Bonitz R G. On robust impedance force control of robot manipulators // Proceedings of International Conference on Robotics and Automation. Albuquerque, 1997: 2057
[61]	Xu G Z, Song A G, Li H J. Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural network. J Intell Robotic Syst, 2011, 62(3-4): 501 doi: 10.1007/s10846-010-9462-3
[62]	Yang C G, Ganesh G, Haddadin S, et al. Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Trans Robotics, 2011, 27(5): 918 doi: 10.1109/TRO.2011.2158251
[63]	Ficuciello F, Villani L, Siciliano B. Variable impedance control of redundant manipulators for intuitive human–robot physical interaction. IEEE Trans Robotics, 2015, 31(4): 850 doi: 10.1109/TRO.2015.2430053
[64]	Li X, Liu Y H, Yu H Y. Iterative learning impedance control for rehabilitation robots driven by series elastic actuators. Automatica, 2018, 90: 1 doi: 10.1016/j.automatica.2017.12.031
[65]	Sun T R, Peng L, Cheng L, et al. Composite learning enhanced robot impedance control. IEEE Trans Neural Netw Learn Syst, 2020, 31(3): 1052 doi: 10.1109/TNNLS.2019.2912212
[66]	Li Z J, Huang B, Ajoudani A, et al. Asymmetric bimanual control of dual-arm exoskeletons for human-cooperative manipulations. IEEE Trans Robotics, 2018, 34(1): 264 doi: 10.1109/TRO.2017.2765334
[67]	Li Z J, Huang B, Ye Z F, et al. Physical human–robot interaction of a robotic exoskeleton by admittance control. IEEE Trans Ind Electron, 2018, 65(12): 9614 doi: 10.1109/TIE.2018.2821649
[68]	Sadrfaridpour B, Wang Y. Collaborative assembly in hybrid manufacturing cells: An integrated framework for human–robot interaction. IEEE Trans Autom Sci Eng, 2018, 15(3): 1178 doi: 10.1109/TASE.2017.2748386
[69]	Peternel L, Tsagarakis N, Ajoudani A. A human–robot co-manipulation approach based on human sensorimotor information. IEEE Trans Neural Syst Rehabilitation Eng, 2017, 25(7): 811 doi: 10.1109/TNSRE.2017.2694553
[70]	He W, Li J S, Yan Z C, et al. Bidirectional human-robot bimanual handover of big planar object with vertical posture. IEEE Trans Autom Sci Eng, 3480, PP(99): 1
[71]	Corteville B, Aertbelien E, Bruyninckx H, et al. Human-inspired robot assistant for fast point-to-point movements // 2007 IEEE International Conference on Robotics and Automation. Rome, 2007: 3639
[72]	Huang J, Huo W G, Xu W X, et al. Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition. IEEE Trans Autom Sci Eng, 2015, 12(4): 1257 doi: 10.1109/TASE.2015.2466634
[73]	Medina J R, Lawitzky M, M?rtl A, et al. An experience-driven robotic assistant acquiring human knowledge to improve haptic cooperation // 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Francisco, 2011: 2416
[74]	Wakita K, Huang J, Di P, et al. Human-walking-intention-based motion control of an omnidirectional-type cane robot. IEEE/ASME Trans Mechatron, 2013, 18(1): 285 doi: 10.1109/TMECH.2011.2169980
[75]	Peternel L, Tsagarakis N, Ajoudani A. Towards multi-modal intention interfaces for human-robot co-manipulation // 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon, 2016: 2663
[76]	Warrier R B, Devasia S. Iterative learning from novice human demonstrations for output tracking. IEEE Trans Hum Mach Syst, 2016, 46(4): 510 doi: 10.1109/THMS.2016.2545243
[77]	Mainprice J, Hayne R, Berenson D. Goal set inverse optimal control and iterative replanning for predicting human reaching motions in shared workspaces. IEEE Trans Robotics, 2016, 32(4): 897 doi: 10.1109/TRO.2016.2581216
[78]	Li Y N, Ge S S. Human–robot collaboration based on motion intention estimation. IEEE/ASME Trans Mechatron, 2014, 19(3): 1007 doi: 10.1109/TMECH.2013.2264533
[79]	Li Y N, Tee K P, Yan R, et al. A framework of human–robot coordination based on game theory and policy iteration. IEEE Trans Robotics, 2016, 32(6): 1408 doi: 10.1109/TRO.2016.2597322
[80]	Cohn D A, Ghahramani Z, Jordan M I. Active learning with statistical models. J Artif Intell Res, 1996, 4: 129 doi: 10.1613/jair.295
[81]	Calinon S, Pistillo A, Caldwell D G. Encoding the time and space constraints of a task in explicit-duration Hidden Markov Model // 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Francisco, 2011: 3413
[82]	Park D W, Kwon J, Lee K M. Robust visual tracking using autoregressive hidden Markov Model // 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, 2012: 1964
[83]	Calinon S, Li Z B, Alizadeh T, et al. Statistical dynamical systems for skills acquisition in humanoids // 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012). Osaka, 2012: 323
[84]	Wiest J, H?ffken M, Kre?el U, et al. Probabilistic trajectory prediction with Gaussian mixture models // 2012 IEEE Intelligent Vehicles Symposium. Madrid, 2012: 141
[85]	Wang J M, Fleet D J, Hertzmann A. Gaussian process dynamical models for human motion. IEEE Trans Pattern Anal Mach Intell, 2008, 30(2): 283 doi: 10.1109/TPAMI.2007.1167
[86]	Wang Y W, Sheng Y X, Wang J, et al. Optimal collision-free robot trajectory generation based on time series prediction of human motion. IEEE Robotics Autom Lett, 2018, 3(1): 226 doi: 10.1109/LRA.2017.2737486
[87]	Huang Z, Hasan A, Shin K, et al. Long-term pedestrian trajectory prediction using mutable intention filter and warp LSTM. IEEE Robotics Autom Lett, 2021, 6(2): 542 doi: 10.1109/LRA.2020.3047731
[88]	Zeng C, Yang C G, Li Q, et al. Research progress on human-robot skill transfer. Acta Autom Sin, 2019, 45(10): 1813 曾超, 楊辰光, 李強, 等. 人–機器人技能傳遞研究進展. 自動化學報, 2019, 45(10):1813
[89]	Khansari-Zadeh S M, Billard A. Learning stable nonlinear dynamical systems with Gaussian mixture models. IEEE Trans Robotics, 2011, 27(5): 943 doi: 10.1109/TRO.2011.2159412
[90]	Paraschos A, Daniel C, Peters J, et al. Using probabilistic movement primitives in robotics. Auton Robots, 2018, 42(3): 529 doi: 10.1007/s10514-017-9648-7
[91]	Rozo L, Calinon S, Caldwell D G, et al. Learning physical collaborative robot behaviors from human demonstrations. IEEE Trans Robotics, 2016, 32(3): 513 doi: 10.1109/TRO.2016.2540623
[92]	Wang P, Zhu J X, Feng W, et al. Robot learning from human demonstration of peg-in-hole task // 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Tianjin, 2018: 318
[93]	Ijspeert A J, Nakanishi J, Hoffmann H, et al. Dynamical movement primitives: Learning attractor models for motor behaviors. Neural Comput, 2013, 25(2): 328 doi: 10.1162/NECO_a_00393
[94]	Kulvicius T, Ning K J, Tamosiunaite M, et al. Joining movement sequences: Modified dynamic movement primitives for robotics applications exemplified on handwriting. IEEE Trans Robotics, 2012, 28(1): 145 doi: 10.1109/TRO.2011.2163863
[95]	Colomé A, Torras C. Dimensionality reduction for dynamic movement primitives and application to bimanual manipulation of clothes. IEEE Trans Robotics, 2018, 34(3): 602 doi: 10.1109/TRO.2018.2808924
[96]	Yang C G, Zeng C, Fang C, et al. A DMPs-based framework for robot learning and generalization of humanlike variable impedance skills. IEEE/ASME Trans Mechatron, 2018, 23(3): 1193 doi: 10.1109/TMECH.2018.2817589
[97]	Rückert E, d'Avella A. Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems. Front Comput Neurosci, 2013, 7: 138
[98]	Deni?a M, Gams A, Ude A, et al. Learning compliant movement primitives through demonstration and statistical generalization. IEEE/ASME Trans Mechatron, 2016, 21(5): 2581 doi: 10.1109/TMECH.2015.2510165
[99]	Si W Y, Wang N, Yang C G. Composite dynamic movement primitives based on neural networks for human-robot skill transfer. Neural Comput Appl, 2021: 1
[100]	Lu Z Y, Wang N, Yang C G. A constrained DMPs framework for robot skills learning and generalization from human demonstrations. IEEE/ASME Trans Mechatron, 7022, PP(99): 1
[101]	Arulkumaran K, Deisenroth M P, Brundage M, et al. Deep reinforcement learning: A brief survey. IEEE Signal Process Mag, 2017, 34(6): 26 doi: 10.1109/MSP.2017.2743240
[102]	Pastor P, Kalakrishnan M, Chitta S, et al. Skill learning and task outcome prediction for manipulation // 2011 IEEE International Conference on Robotics and Automation. Shanghai, 2011: 3828
[103]	Kormushev P, Calinon S, Caldwell D G. Robot motor skill coordination with EM-based Reinforcement Learning // 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei, 2010: 3232
[104]	Kormushev P, Calinon S, Saegusa R, et al. Learning the skill of archery by a humanoid robot iCub // 2010 10th IEEE-RAS International Conference on Humanoid Robots. Nashville, 2010: 417
[105]	Rosenstein M T, Barto A G, van Emmerik R E A. Learning at the level of synergies for a robot weightlifter. Robotics Auton Syst, 2006, 54(8): 706 doi: 10.1016/j.robot.2006.03.002
[106]	Abbeel P, Ng A Y. Apprenticeship learning via inverse reinforcement learning // Proceedings of the Twenty-First International Conference on Machine Learning. Banff, 2004: 1
[107]	Ziebart B D, Maas A L, Bagnell J A, et al. Maximum entropy inverse reinforcement learning // Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence. Chicago, 2008: 1433
[108]	Levine S, Popovic Z, Koltun V. Feature construction for inverse reinforcement learning // 2010 Conference and Workshop on Neural Information Processing Systems. Vancouver, 2010, 23: 1342
[109]	Ratliff N, Bradley D, Bagnell J A, et al. Boosting structured prediction for imitation learning // 2006 Conference of the Proceedings International on Neural Information Processing Systems. Vancouver, 2006: 1153
[110]	Krishnan S, Garg A, Liaw R, et al. SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards. Int J Robotics Res, 2019, 38(2-3): 126 doi: 10.1177/0278364918784350
[111]	Ho J, Ermon S. Generative adversarial imitation learning // 30th Conference on Neural Information Processing Systems (NIPS 2016). Barcelona, 2016: 4565