REN Danmei, BIAN Feifei. A Survey of Control Methods for Robot Compliant Behaviour[J]. INFORMATION AND CONTROL, 2024, 53(4): 433-452. DOI: 10.13976/j.cnki.xk.2024.4003
Citation: REN Danmei, BIAN Feifei. A Survey of Control Methods for Robot Compliant Behaviour[J]. INFORMATION AND CONTROL, 2024, 53(4): 433-452. DOI: 10.13976/j.cnki.xk.2024.4003

A Survey of Control Methods for Robot Compliant Behaviour

More Information
  • Received Date: February 29, 2024
  • Revised Date: June 03, 2024
  • Accepted Date: April 17, 2024
  • Robot compliant behavior means that the robot can dynamically adjust its own motion strategy, so as to show certain compliance to the physical interaction from human beings or environments.We review the control methods for robot compliant behavior. Firstly, the robot compliant control methods are classified according to different control loops. Then, the methods implemented in motion control loop, path planning loop and task scheduling loop are collected and summarized. The different characteristics of robot compliance brought by each method are analyzed. Finally, several typical applications of robot compliant control are summarized, and the future trend of robot compliant control technology is prospected, in order to provide new ideas and directions for the research of robot compliant control.

  • [1]
    LOSEY D P, MCDONALD C G, BATTAGLIA E, et al. A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction[J/OL]. Applied Mechanics Reviews, 2018, 70(1)[2024-06-21]. https://www.researchgate.net/publication/322745295. DOI: 10.1115/1.4039145.
    [2]
    SELVAGGIO M, COGNETTI M, NIKOLAIDIS S, et al. Autonomy in physical human-robot interaction: A brief survey[J]. IEEE Robotics and Automation Letters, 2021, 6(4): 7989-7996. doi: 10.1109/LRA.2021.3100603
    [3]
    HOGAN N. Impedance control: An approach to manipulation: Part Ⅰ-Theory[J]. Journal of Dynamic Systems, Measurement and Control, 1985, 107(1): 1-7. doi: 10.1115/1.3140702
    [4]
    HOGAN N. Impedance control: An approach to manipulation: Part Ⅱ-Implementation[J]. Journal of Dynamic Systems, Measurement and Control. 1985, 107(1): 8-16. doi: 10.1115/1.3140713
    [5]
    HOGAN N. Impedance control: An approach to manipulation: Part Ⅲ-Applications[J]. Journal of Dynamic Systems, Measurement and Control. 1985, 107(1): 17-24. doi: 10.1115/1.3140701
    [6]
    FERRAGUTI F, SECCHI C, FANTUZZI C. A tank-based approach to impedance control with variable stiffness[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2013: 4948-4953.
    [7]
    RASHAD R, ENGELEN J B C, STRAMIGIOLI S. Energy tank-based wrench/impedance control of a fully-actuated hexarotor: A geometric port-hamiltonian approach[C]//International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2019: 6418-6424.
    [8]
    OTT C, MUKHERJEE R, NAKAMURA Y. A hybrid system framework for unified impedance and admittance control[J]. Journal of Intelligent & Robotic Systems, 2015, 78(3): 359-375.
    [9]
    KEEMINK A Q L, VAN DER KOOIJ H, STIENEN A H A. Admittance control for physical human-robot interaction[J]. The International Journal of Robotics Research, 2018, 37(11): 1421-1444. doi: 10.1177/0278364918768950
    [10]
    KANG G, OH H S, SEO J K, et al. Variable admittance control of robot manipulators based on human intention[J]. IEEE/ASME Transactions on Mechatronics, 2019, 24(3): 1023-1032. doi: 10.1109/TMECH.2019.2910237
    [11]
    李枭. 基于七轴机器人iiwa的人机协作技术研究[J]. 电子技术, 2021, 50(3): 32-33. https://www.cnki.com.cn/Article/CJFDTOTAL-DZJS202103015.htm

    LI X. Study on human machine cooperation technology based on seven axis robot iiwa[J]. Electronic Technology, 2021, 50(3): 32-33. https://www.cnki.com.cn/Article/CJFDTOTAL-DZJS202103015.htm
    [12]
    LIPPIELLO V, SICILIANO B, VILLANI L. A position-based visual impedance control for robot manipulators[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2007: 2068-2073.
    [13]
    CACCAVALE F, CHIACCHIO P, MARINO A, et al. Six-dof impedance control of dual-arm cooperative manipulators[J]. IEEE/ASME Transactions on Mechatronics, 2008, 13(5): 576-586. doi: 10.1109/TMECH.2008.2002816
    [14]
    EIBAND T, SAVERIANO M, LEE D. Learning haptic exploration schemes for adaptive task execution[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2019: 7048-7054.
    [15]
    LI Y, GE S S, YANG C, et al. Model-free impedance control for safe human-robot interaction[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2011: 6021-6026.
    [16]
    LI Y, SAM G S, YANG C. Learning impedance control for physical robot-environment interaction[J]. International Journal of Control, 2012, 85(2): 182-193. doi: 10.1080/00207179.2011.642309
    [17]
    MAGRINI E, FLACCO F, DE LUCA A. Estimation of contact forces using a virtual force sensor[C]//IEEE International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2014: 2126-2133.
    [18]
    MAGRINI E, FLACCO F, DE LUCA A. Control of generalized contact motion and force in physical human-robot interaction[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2015: 2298-2304.
    [19]
    SALDARRIAGA C, CHAKRABORTY N, KAO I. Joint space stiffness and damping for Cartesian and null space impedance control of redundant robotic manipulators[C]//Proceedings in Advanced Robotics. Piscataway, USA: IEEE, 2019: 410-426.
    [20]
    HUO J, RU H, YANG B, et al. Air-chamber-based soft six-axis force/torque sensor for human-robot interaction[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73(2024): 1-12.
    [21]
    WANG C, LIU C, SHANG F, et al. Tactile sensing technology in bionic skin: A review[J]. Biosensors and Bioelectronics, 2023, 220: 1-18.
    [22]
    PAN M, SU T, LIANG K, et al. Sensorless force estimation of teleoperation system based on multilayer depth extreme learning machine[J]. Applied Soft Computing, 2024, 157(111494): 1-11.
    [23]
    TIAN Y, ZHANG C, JIANG S, et al. Noncontact cable force estimation with unmanned aerial vehicle and computer vision[J]. Computer-Aided Civil and Infrastructure Engineering, 2021, 36(1): 73-88. doi: 10.1111/mice.12567
    [24]
    ALBU-SCHAFFER A, HIRZINGER G. Cartesian impedance control techniques for torque controlled light-weight robots[C]//International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2002: 657-663.
    [25]
    SALISBURY J K. Active stiffness control of a manipulator in cartesian coordinates[C]//IEEE Conference on Decision and Control. Piscataway, USA: IEEE, 1980: 95-100.
    [26]
    CHEN S F, KAO I. Conservative congruence transformation for joint and Cartesian stiffness matrices of robotic hands and fingers[J]. The International Journal of Robotics Research, 2000, 19(9): 835-847. doi: 10.1177/02783640022067201
    [27]
    SONG P, YU Y, ZHANG X. Impedance control of robots: An overview[C]//2nd International Conference on Cybernetics, Robotics and Control. Piscataway, USA: IEEE, 2017: 51-55.
    [28]
    CALANCA A, MURADORE R, FIORINI P. A review of algorithms for compliant control of stiff and fixed-compliance robots[J]. IEEE/ASME Transactions on Mechatronics, 2015, 21(2): 613-624.
    [29]
    SONG P, YU Y, ZHANG X. A tutorial survey and comparison of impedance control on robotic manipulation[J]. Robotica, 2019, 37(5): 801-836. doi: 10.1017/S0263574718001339
    [30]
    BALATTI P, KANOULAS D, TSAGARAKIS N G, et al. Towards robot interaction autonomy: explore, identify, and interact[C]//International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2019: 9523-9529.
    [31]
    PARENT D, COLOMé A, TORRAS C. Variable impedance control in Cartesian latent space while avoiding obstacles in null space[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2020: 9888-9894.
    [32]
    LEE D, OTT C. Incremental kinesthetic teaching of motion primitives using the motion refinement tube[J]. Autonomous Robots, 2011, 31(2): 115-131.
    [33]
    BIAN F F, LI R F, LIANG P D. SVM based simultaneous hand movements classification using sEMG signals[C]//IEEE International Conference on Mechatronics and Automation. Piscataway, USA: IEEE, 2017: 427-432.
    [34]
    PETERNEL L, PETRIC T, OZTOP E, et al. Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach[J]. Autonomous Robots, 2014, 36(1): 123-136.
    [35]
    VOGEL J, CASTELLINI C, VAN DER SMAGT P. EMG-based teleoperation and manipulation with the DLR LWR-Ⅲ[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2011: 672-678.
    [36]
    YANG C, ZENG C, FANG C, et al. A dmps-based framework for robot learning and generalization of humanlike variable impedance skills[J]. IEEE/ASME Transactions on Mechatronics, 2018, 23(3): 1193-1203. doi: 10.1109/TMECH.2018.2817589
    [37]
    BIAN F F, REN D M, LI R F, et al. Improving stability in physical human-robot interaction by estimating human hand stiffness and a vibration index[J]. Industrial Robot: An International Journal, 2019, 46(4): 529-540. doi: 10.1108/IR-05-2018-0111
    [38]
    BIAN F F, LI R F, REN D M. Variable admittance control improving stability in physical human-robot interaction[C]//IEEE International Conference on Information and Automation. Piscataway, USA: IEEE, 2018: 1485-1490.
    [39]
    TSUMUGIWA T, YOKOGAWA R, HARA K. Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2002: 644-650.
    [40]
    WAKITA K, HUANG J, DI P, et al. Human-walking-intention-based motion control of an omnidirectional-type cane robot[J]. IEEE/ASME Transactions on Mechatronics, 2013, 18(1): 285-296. doi: 10.1109/TMECH.2011.2169980
    [41]
    VAROL H A, SUP F, GOLDFARB M. Multiclass real-time intent recognition of a powered lower limb prosthesis[J]. IEEE Transactions on Biomedical Engineering, 2010, 57(3): 542-551. doi: 10.1109/TBME.2009.2034734
    [42]
    PHILIPS J, MILLAN J D R, VANACKER G, et al. Adaptive shared control of a brain-actuated simulated wheelchair[C]//10th IEEE International Conference on Rehabilitation Robotics. Piscataway, USA: IEEE, 2007: 408-414.
    [43]
    CARLSON T, MILLAN J D R. Brain controlled wheelchairs: A robotic architecture[J]. IEEE Robotics and Automation Magazine, 2013, 20(1): 65-73. doi: 10.1109/MRA.2012.2229936
    [44]
    REBSAMEN B, GUAN C, ZHANG H, et al. A brain controlled wheelchair to navigate in familiar environments[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010, 18(6): 590-598. doi: 10.1109/TNSRE.2010.2049862
    [45]
    AARNO D, EKVALL S, KRAGIC D. Adaptive virtual fixtures for machine-assisted teleoperation tasks[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2005: 1139-1144.
    [46]
    YU W, ALQASEMI R, DUBEY R, et al. Tele-manipulation assistance based on motion intention recognition[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2005: 1121-1126.
    [47]
    BIAN F F, REN D M, LI R F, et al. Dynamical system based variable admittance control for physical human-robot interaction[J]. Industrial Robot: An International Journal, 2020, 47(4): 623-635. doi: 10.1108/IR-12-2019-0258
    [48]
    WOLBRECHT E T, CHAN V, REINKENSMEYER D J, et al. Optimizing compliant model based robotic assistance to promote neuro rehabilitation[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2008, 16(3): 286-297. doi: 10.1109/TNSRE.2008.918389
    [49]
    RAUTER G, SIGRIST R, MARCHAL-CRESPO L, et al. Assistance or challenge? filling a gap in user-cooperative control[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2011: 3068-3073.
    [50]
    PEHLIVAN A U, LOSEY D P, O'MALLEY M K. Minimal assist-as-needed controller for upper limb robotic rehabilitation[J]. IEEE Transactions on Robotics, 2016, 32(1): 113-124. doi: 10.1109/TRO.2015.2503726
    [51]
    EMKEN J L, BOBROW J E, REINKENSMEYER D J. Robotic movement training as an optimization problem: Designing a controller that assists only as needed[C]//9th International Conference on Rehabilitation Robotics. Piscataway, USA: IEEE, 2005: 307-312.
    [52]
    EVRARD P, KHEDDAR A. Homotopy switching model for dyad haptic interaction in physical collaborative tasks[C]//Symposium on Eurohaptics Conference. Piscataway, USA: IEEE, 2009: 45-50.
    [53]
    MEDINA J R, LORENZ T, HIRCHE S. Synthesizing anticipatory haptic assistance considering human behavior uncertainty[J]. IEEE Transactions on Robotics, 2015, 31(1): 180-190. doi: 10.1109/TRO.2014.2387571
    [54]
    LI Y, TEE K P, CHAN W L, et al. Continuous role adaptation for human-robot shared control[J]. IEEE Transactions on Robotics, 2015, 31(3): 672-681. http://www1.i2r.a-star.edu.sg/~kptee/TRO-role-adaptation.pdf
    [55]
    THOBBI A, GU Y, SHENG W. Using human motion estimation for human-robot cooperative manipulation[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2011: 2873-2878.
    [56]
    DRAGAN A D, SRINIVASA S S. A policy blending formalism for shared control[J]. The International Journal of Robotics Research, 2013, 32(7): 790-805.
    [57]
    IJSPEERT A J, NAKANISHI J, HOFFMANN H, et al. Dynamical movement primitives: Learning attractor models for motor behaviors[J]. Neural Computation, 2013, 25(2): 328-373. http://users.wpi.edu/~zli11/teaching/rbe595_2017/Assignment/Assignment12.pdf
    [58]
    COLOME A, TORRAS C. Dimensionality reduction for dynamic movement primitives and application to bimanual manipulation of clothes[J]. IEEE Transactions on Robotics, 2018, 34(3): 602-615.
    [59]
    PASTOR P, HOFFMANN H, ASFOUR T, et al. Learning and generalization of motor skills by learning from demonstration[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2009: 763-768.
    [60]
    YANG C, ZENG C, FANG C, et al. A dmps-based framework for robot learning and generalization of humanlike variable impedance skills[J]. IEEE/ASME Transactions on Mechatronics, 2018, 23(3): 1193-1203.
    [61]
    KOC O, MAEDA G, NEUMANN G, et al. Optimizing robot striking movement primitives with Iterative Learning Control[C]//15th IEEE-RAS International Conference on Humanoid Robots. Piscataway, USA: IEEE, 2015: 80-87.
    [62]
    BIAN F F, REN D M, LI R F, et al. An extended DMP framework for robot learning and improving variable stiffness manipulation[J]. Assembly Automation, 2019, 40(1): 85-94.
    [63]
    MAEDA G J, NEUMANN G, EWERTON M, et al. Probabilistic movement primitives for coordination of multiple human-robot collaborative tasks[J]. Autonomous Robots, 2017, 41(3): 593-612.
    [64]
    GAMS A, NEMEC B, IJSPEERT A J, et al. Coupling movement primitives: Interaction with the environment and bimanual tasks[J]. IEEE Transactions on Robotics, 2014, 30(4): 816-830.
    [65]
    NEMEC B, LIKAR N, GAMS A, et al. Human robot cooperation with compliance adaptation along the motion trajectory[J]. Autonomous Robots, 2018, 42(5): 1023-1035.
    [66]
    KHANSARI-ZADEH S M, BILLARD A. Learning stable non-linear dynamical systems with Gaussian mixture models[J]. IEEE Transaction on Robotics, 2011, 27(5): 943-957.
    [67]
    KHANSARI-ZADEH S M, KHATIB O. Learning potential functions from human demonstrations with encapsulated dynamic and compliant behaviors[J]. Autonomous Robots, 2017, 41(1): 45-69.
    [68]
    KHANSARI-ZADEH S M, KLINGBEIL E, KHATIB O. Adaptive human-inspired compliant contact primitives to perform surface-surface contact under uncertainty[J]. International Journal of Robotics Research, 2016, 35(13): 1651-1675.
    [69]
    KRONANDER K, KHANSARI-ZADEH S M, BILLARD A. Incremental motion learning with locally modulated dynamical systems[J]. Robotics and Autonomous Systems, 2015, 70: 52-62.
    [70]
    KHANSARI-ZADEH, BILLARD A. A dynamical system approach to real-time obstacle avoidance[J]. Autonomous Robots, 2012, 32(4): 433-454.
    [71]
    KHORAMSHAHI M, LAURENS A, TRIQUET T, et al. From human physical interaction to online motion adaptation using parameterized dynamical systems[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2018: 1361-1366.
    [72]
    KHORAMSHAHI M, BILLARD A. A dynamical system approach for detection and reaction to human guidance in physical human-robot interaction[J]. Autonomous Robots, 2020, 44(8): 1411-1429.
    [73]
    KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 1985: 500-505.
    [74]
    MONTIEL O, OROZCO ROSAS U, SEPúLVEDA R. Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles[J]. Expert Systems with Applications, 2015, 42(12): 5177-5191.
    [75]
    TRIHARMINTO H H, WAHYUNGGORO O, ADJI T B, et al. An integrated artificial potential field path planning with kinematic control for non-holonomic mobile robot[J]. International Journal on Advanced Science, Engineering and Information Technology, 2016, 6(4): 410-418.
    [76]
    TRIHARMINTO H H, WAHYUNGGORO O, ADJI T B, et al. Local information using stereo camera in artificial potential field based path planning[J]. IAENG International Journal of Computer Science, 2017, 44(3): 316-326.
    [77]
    MONTIEL O, OROZCO-ROSAS U, SEPULVEDA R. Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles[J]. Expert Systems with Applications, 2015, 42(12): 5177-5191.
    [78]
    ISWANTO I, MA'ARIF A, WAHYUNGGORO O, et al. Artificial potential field algorithm implementation for quadrotor path planning[J]. International Journal of Advanced Computer Science and Applications, 2019, 10(8): 575-585.
    [79]
    YUN G, WEI Z, GONG F, et al. Dynamic path planning for underwater vehicles based on modified artificial potential field method[C]//4th International Conference on Digital Manufacturing and Automation. Piscataway, USA: IEEE, 2013: 518-521.
    [80]
    ZHONG Q, JIE Z, TONG C. Tracking for humanoid robot based on Kinect[C]//International Conference on Mechatronics and Control. Piscataway, USA: IEEE, 2014: 1191-1194.
    [81]
    IGARASHI H, KAKIKURA M. Path and posture planning for walking robots by artificial potential field method[C]//IEEE International Conference on Robotics & Automation. Piscataway, USA: IEEE, 2004: 2165-2170.
    [82]
    SANCHO-PRADEL D L, SAAJ C M. Assessment of artificial potential field methods for navigation of planetary rovers[C]//2009 European Control Conference. Piscataway, USA: IEEE, 2009: 3027-3032.
    [83]
    WANG W, ZHU M, WANG X, et al. An improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators[J]. International Journal of Advanced Robotic Systems, 2018, 15(5): 1-13.
    [84]
    DU G, LONG S, LI F, et al. Active collision avoidance for human-robot interaction with UKF, expert system, and artificial potential field method[J]. Frontiers in Robotics and AI, 2018, 5: 1-11.
    [85]
    BROZ F, NOURBAKHSH I, SIMMONS R. Designing POMDP models of socially situated tasks[C]//IEEE International Symposium on Robot and Human Interactive Communication. Piscataway, USA: IEEE, 2011: 39-46.
    [86]
    HOROWITZ M, BURDICK J. Interactive non-prehensile manipulation for grasping via POMDPs[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2013: 3257-3264.
    [87]
    HSIAO K, KAELBLING L P, LOZANO-PEREZ T. Grasping POMDPs[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2007: 4685-4692.
    [88]
    KOVAL M C, POLLARD N S, SRINIVASA S S. Pre-and-post contact policy decomposition for planar contact manipulation under uncertainty[J]. International Journal of Robotics Research, 2016, 35(1/2/3): 244-264.
    [89]
    MONSO P, ALENY'A G, TORRAS C. POMDP approach to robotized clothes separation[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2012: 1324-1329.
    [90]
    GARG F, HSU D, LEE W S. Learning to grasp under uncertainty using POMDPs[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2019: 2751-2757.
    [91]
    LI J K, HSU D, LEE W S. Act to see and see to act: POMDP planning for objects search in clutter[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2016: 5701-5707.
    [92]
    XIAO Y, KATT S, TEN PAS A, et al. Online planning for target object search in clutter under partial observability[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2019: 8241-8247.
    [93]
    PAJARINEN J, KYRKI V. Robotic manipulation in object composition space[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2014: 1-6.
    [94]
    PAJARINEN J, KYRKI V. Robotic manipulation of multiple objects as a POMDP[J]. Artificial Intelligence, 2017, 247(2017): 213-228.
    [95]
    PAJARINEN J, LUNDELL J, KYRKI V. POMDP planning under object composition uncertainty: Application to robotic manipulation[J]. IEEE Transactions on Robotics, 2023, 39(1): 41-56.
    [96]
    BAJCSY A, LOSEY D P, O'MALLEY M K, et al. Learning robot objectives from physical human interaction[J]. Journal of Machine Learning Research, 2017: 217-226.
    [97]
    CALINON S, GUENTER F, BILLARD A. On learning, representing, and generalizing a task in a humanoid robot[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2007, 37(2): 286-298
    [98]
    CALINON S, LI Z B, ALIZADEH T, et al. Statistical dynamical systems for skills acquisition in humanoids[C]//12th IEEE-RAS International Conference on Humanoid Robots. Piscataway, USA: IEEE, 2012: 323-329.
    [99]
    PISTILLO A, CALINON S, CALDWELL D G. Bilateral physical interaction with a robot manipulator through a weighted combination of flow fields[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2011: 3047-3052.
    [100]
    BANDYOPADHYAY T, JIE C Z, HSU D, et al. Intention-aware pedestrian avoidance[C]//Experimental Robotics. Berlin, Germany: Springer, 2013: 963-977.
    [101]
    RAVICHANDAR H C, DANI A. Human intention inference and motion modeling using approximate EM with online learning[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2015: 1819-1824.
    [102]
    WANG W, LI R, CHEN Y, et al. Human intention prediction in human-robot collaborative tasks[C]//ACM/IEEE International Conference on Human-Robot Interaction. Piscataway, USA: IEEE, 2018: 279-280.
    [103]
    丁其川, 熊安斌, 赵新刚, 等. 基于表面肌电的运动意图识别方法研究及应用综述[J]. 自动化学报, 2016, 42(1): 13-25. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201601002.htm

    DING Q C, XIONG A B, ZHAO X G, et al. A review on researches and applications of sEMG-based motion intent recognition methods[J]. Acta Automatica Sinica, 2016, 42(1): 13-25. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201601002.htm
    [104]
    BUSSY A, GERGONDET P, KHEDDAR A, et al. Proactive behavior of a humanoid robot in a haptic transportation task with a human partner[C]//21st IEEE International Symposium on Robot and Human Interactive Communication. Piscataway, USA: IEEE, 2012: 962-967.
    [105]
    CALINON S, BRUNO D, CALDWELL D G. A task-parameterized probabilistic model with minimal intervention control[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2014: 3339-3344.
    [106]
    EWERTON M, NEUMANN G, LIOUTIKOV R, et al. Learning multiple collaborative tasks with a mixture of interaction primitives[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2015: 1535-1542.
    [107]
    LEE S H, SUH I H, CALINON S, et al. Autonomous framework for segmenting robot trajectories of manipulation task[J]. Autonomous Robots, 2015, 38(2): 107-141.
    [108]
    KHORAMSHAHI M, BILLARD A. A dynamical system approach to task-adaptation in physical human-robot interaction[J]. Autonomous Robots, 2019, 43: 927-946.
    [109]
    李林峰, 解永春. 空间机器人操作: 一种多任务学习视角[J]. 中国空间科学技术, 2022, 42(3): 10-24. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGKJ202203002.htm

    LI L F, XIE Y C. Space robotic manipulation: A multi-task learning perspective[J]. Chinese Space Science and Technology, 2022, 42 (3): 10-24. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGKJ202203002.htm
    [110]
    ROSS S, GORDON G, BAGNELL D. A reduction of imitation learning and structured prediction to no-regret online learning[C]//14th International Conference on Artificial Intelligence and Statistics. New York, USA: PMLR, 2011: 627-635.
    [111]
    LASKEY M, LEE J, FOX R, et al. DART: Noise injection for robust imitation learning[C]//1st Conference on Robot Learning. New York, USA: PMLR, 2017: 143-156.
    [112]
    SCHMIDHUBER J. On learning to think: Algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models[EB/OL]. (2015-11-30)[2024-03-22]. https://arxiv.org/abs/1511.09249.
    [113]
    ANGELOV D, HRISTOV Y, BURKE M, et al. Composing diverse policies for temporally extended tasks[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 2658-2665.
    [114]
    NACHUM O, AHN M, PONTE H, et al. Multi-agent manipulation via locomotion using hierarchical sim2real[C]//3rd Conference on Robot Learning. New York, USA: PMLR, 2019: 110-121.
    [115]
    FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]//34th International Conference on Machine Learning. New York, USA: PMLR, 2017: 1126-1135.
    [116]
    YU T, QUILLEN D, HE Z, et al. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning[C]//3rd Conference on Robot Learning. New York, USA: PMLR, 2019: 1094-1100.
    [117]
    SOROKIN A Y, BURTSEV M S. Continual and multitask reinforcement learning with shared episodic memory[EB/OL]. (2019-05-07)[2024-03-22]. https://arxiv.org/abs/1905.02662.
    [118]
    TRAORé R, CASELLES-DUPRé H, LESORT T, et al. Continual reinforcement learning deployed in real-life using policy distillation and Sim2Real transfer[EB/OL]. (2019-01-11)[2024-03-22]. https://arxiv.org/abs/1906.04452.
    [119]
    PORTELAS R, HOFMANN K, OUDEYER P Y. Trying AGAIN instead of trying longer: Prior learning for automatic curriculum learning[EB/OL]. (2020-04-07)[2021-07-15]. https://arxiv.org/abs/2004.03168.
    [120]
    GOPINATHAN S, ÖTTING S K, STEIL J J. A user study on personalized stiffness control and task specificity in physical human-robot interaction[J]. Frontiers in Robotics and AI, 2017, 4: 1-16.
    [121]
    RAESSA M, CHEN J, WAN W, et al. Human-in-the-loop robotic manipulation planning for collaborative assembly[J]. IEEE Transactions on Automation Science and Engineering, 2020, 17(4): 1800-1813.
    [122]
    SHAHBAZI M, ATASHZA S F, TAVAKOLI M, et al. Robotics-assisted mirror rehabilitation therapy: A therapist-in-the-loop assist-as-needed architecture[J]. IEEE-ASME Transactions on Mechatronics, 2016, 21(4): 1954-1965.
    [123]
    KIM B, DESHPANDE A D. An upper-body rehabilitation exoskeleton harmony with an anatomical shoulder mechanism[J]. International Journal of Robotics Research, 2017, 36(4): 414-435.
    [124]
    卢浩, 王洪波, 冯永飞. 下肢康复机器人人机耦合动力学建模和主动柔顺控制[J]. 机械工程学报, 2022, 58(7): 32-43. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202207004.htm

    LU H, WANG H B, FENG Y F. Human-machine coupling dynamics modeling and active compliance control of lower limb rehabilitation robot[J]. Journal of Mechanical Engineering, 2022, 58(7): 32-43. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202207004.htm
    [125]
    李康康, 胡锦洋, 邢普, 等. 面向柔顺装配装夹的机器人手腕变刚度机理研究[J]. 机械工程学报, 2022, 58(19): 77-85. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202219008.htm

    LI K K, HU J Y, XING P, et al. Research on variable-stiffness mechanisms of robot wrists for compliant assembling-clamping[J]. Journal of Mechanical Engineering, 2022, 58(19): 77-85. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202219008.htm
    [126]
    陈书清, 李铁民. 基于自适应柔顺控制的航天器部件装配[J]. 清华大学学报(自然科学版), 2023, 63(11): 1808-1819. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB202311011.htm

    CHEN S Q, LI T M. Spacecraft component assembly based on adaptive compliance control[J]. Journal of Tsinghua University (Science and Technology), 2023, 63(11): 1808-1819. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB202311011.htm
    [127]
    SIAN N, SAKAGUCHI T, YOKOI K, et al. Operating humanoid robots in human environments[C]//Workshop: Manipulation for Human Environments. Cambridge, USA: RSS, 2006: 1-6.
    [128]
    PARK D, HOSHI Y, MAHAJAN H, et al. Active robot-assisted feeding with a general-purpose mobile manipulator: Design, evaluation, and lessons learned[J]. Robotics and Autonomous Systems, 2020, 124: 1-17.
    [129]
    罗威, 李明富, 赵文权, 等. 基于力- 位图像学习的工业机器人柔顺装配方法研究[J]. 机械工程学报, 2022, 58(21): 69-77. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202221007.htm

    LUO W, LI M F, ZHAO W Q, et al. Research on flexible assembly method of industrial robot based on force-pose-image learning[J]. Journal of Mechanical Engineering, 2022, 58(21): 69-77. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202221007.htm
    [130]
    许未晴, 陈磊, 隋秀峰等. 脑机接口——脑信息读取与脑活动调控技术[J]. 科学通报, 2023, 68(8): 927-943. https://www.cnki.com.cn/Article/CJFDTOTAL-KXTB202308008.htm

    XU W Q, CHEN L, SUI X F, et al. Brain-computer interface-Brain information reading and activity control[J]. Chinese Science Bulletin, 2023, 68(8): 927-943. https://www.cnki.com.cn/Article/CJFDTOTAL-KXTB202308008.htm
    [131]
    杨赓, 周慧颖, 王柏村. 数字孪生驱动的智能人机协作: 理论、技术与应用[J]. 机械工程学报, 2022, 58(18): 279-291. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202218023.htm

    YANG G, ZHOU H Y, WANG B C. Digital twin-driven smart human-machine collaboration: Theory, enabling technologies and applications[J]. Journal of Mechanical Engineering, 2022, 58(18): 279-291. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202218023.htm
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