Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240300063-5.doi: 10.11896/jsjkx.240300063

• Interdiscipline & Application • Previous Articles     Next Articles

Robot Performance Teaching Demonstration System Based on Imitation Learning

ZHAO Yufei1, JIN Cong2, LIU Xiaoyu3, WANG Jie2, ZHU Yonggui1, LI Bo4   

  1. 1 School of Data Science and Media Intelligence,Communication University of China,Beijing 100000,China
    2 School of Information and Communication Engineering,Communication University of China,Beijing 100000,China
    3 China Philharmonic Orchestra,Beijing 100000,China
    4 School of Electronics and Information,Northwestern Polytechnical University,Xi'an 710000,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:ZHAO Yufei,born in 2000,postgra-duate.Her main research interests include computer vision and deep lear-ning.
    JIN Cong,born in 1986,Ph.D,is a member of CCF(No.C4376M).Her main research interests include reinforcement learning,music AI and audio digital twins.

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Abstract: In recent years,imitation learning has been widely applied in the field of robotics,demonstrating significant potential.At the same time,the application of intelligent systems in the field of education is becoming more and more diversified,and the reasonable application of robots in teaching can improve the teaching effect.If robots can instruct certain professional skills,such as playing musical instruments,it could offer significant convenience for both students and human teachers.Imitation learning is particularly suitable for highly specialized and technically demanding tasks,such as violin performance.However,the introduction of expert demonstrations into the process of dynamic movement primitives(DMP),especially regarding the ambiguity issues like uncertainties in string-changing angles,poses a prominent challenge.Traditional methods of measuring string-changing angles,such as physical measurements,exhibit substantial errors and lack generalization.To address this issue,a new model named fuzzy dynamic movement primitive for teaching(T-FDMP) is proposed.The model is constructed based on Type-2 fuzzy model and principal component analysis(PCA).It utilizes the features obtained from principal component analysis(PCA),specifically the bowing angle,as input for the membership functions(string angles) and simultaneously builds a professional-level music perfor-mance behavior database.Bionic experimental results demonstrate that our T-FDMP model can precisely control the robot for violin performance.Furthermore,it opens up new research directions for imitation learning in other highly specialized and technical domains.

Key words: Imitation learning, Robot control, Type-2 fuzzy model, Smart education, Dynamic movement primitives

CLC Number: 

  • TP242
[1]HUA J,ZENG L C,LI G F,et al.Learning for a robot:Deep reinforcement learning,imitation learning,transfer learning[J].Sensors,2021,21(4):1278.
[2]JAIN A,SHARMA A,PARHI D R.A survey on imitationlearning techniques for end-to-end autonomous vehicles[J].IEEE Transactions on Intelligent Transportation Systems,2022,23(9):14128-14147.
[3]GHASEMIPOUR K S,ZEMEL R,GU S.A Divergence Minimi-zation Perspective on Imitation Learning Methods[J].arXiv:1911.02256,2019.
[4]HU A,CORRADO G,GRIFFITHS N,et al.Model-based imitation learning for urban driving[J].Advances in Neural Information Processing Systems,2022,35:20703-20716.
[5]RAJARAMAN N,YANG L,JIAO J,et al.Toward the fundamental limits of imitation learning[J].Advances in Neural Information Processing Systems,2020,33:2914-2924.
[6]RASHIDINEJAD P,ZHU B,MA C,et al.Bridging offline reinforcement learning and imitation learning:A tale of pessimism[J].Advances in Neural Information Processing Systems,2021,34:11702-11716.
[7]ORSINI M,RAICHUK A,HUSSENOT L,et al.What matters for adversarial imitation learning?[J].Advances in Neural Information Processing Systems,2021,34:14656-14668.
[8]CELEMIN C,PÉREZ-DATTARI R,CHISARI E,et al.Interactive imitation learning in robotics:A survey[J].Foundations and Trends© in Robotics,2022,10(1/2):1-197.
[9]BEHRENS G A,GREEN S B.The ability to identify emotional content of solo improvisations performed vocally and on three different instruments[J].Psychology of Music,1993,21(1):20-33.
[10]WOODY R H.Emotion,imagery and metaphor in the acquisi-tion of musical performance skill[J].Music Education Research,2002,4(2):213-224.
[11]WALTHAM C.The science of string instruments[M].NewYork:Springer,2010.
[12]YOUNG D.The hyperbow controller:Real-time dynamics measurement of violin performance[C]//Proceedings of the 2002 Conference on New Interfaces for Musical Expression.2002:1-6.
[13]SCHOONDERWALDT E,DEMOUCRON M.Extraction ofbowing parameters from violin performance combining motion capture and sensors[J].The Journal of the Acoustical Society of America,2009,126(5):2695-2708.
[14]BEVILACQUA F,RASAMIMANANA N H,FLÉTY E,et al.The augmented violin project:research,composition and performance report[C]//6th International Conference on New Interfaces for Musical Expression(NIME 6).2006:402-406.
[15]CAMPO A,MICHAŁKO A,VAN KERREBROECK B,et al.The assessment of presence and performance in an AR environment for motor imitation learning:A case-study on violinists[J].Computers in Human Behavior,2023,146:107810.
[16]ALAM A.Employing adaptive learning and intelligent tutoring robots for virtual classrooms and smart campuses:reforming education in the age of artificial intelligence[M]//Advanced Computing and Intelligent Technologies:Proceedings of ICACIT 2022.Singapore:Springer Nature Singapore,2022:395-406.
[17]AL HAKIM V G,YANG S H,LIYANAWATTA M,et al.Robots in situated learning classrooms with immediate feedback mechanisms to improve students' learning performance[J].Computers & Education,2022,182:104483.
[18]VALAGKOUTI I A,TROUSSAS C,KROUSKA A,et al.Emotion recognition in human-robot interaction using the NAO robot[J].Computers,2022,11(5):72.
[19]ZHANG Y,ZHANG C,CHENG L,et al.The use of deep lear-ning-based gesture interactive robot in the treatment of autistic children under music perception education[J].Frontiers in Psychology,2022,13:762701.
[20]ZHU J,GIENGER M,KOBER J.Learning task-parameterized skills from few demonstrations[J].IEEE Robotics and Automation Letters,2022,7(2):4063-4070.
[21]IJSPEERT A J,NAKANISHI J,HOFFMANN H,et al.Dynamical movement primitives:learning attractor models for motor behaviors[J].Neural Computer,2013,25(2):328-373.
[22]ZHANG C Z.Study on Dynamic Vibration and Acoustic Behaviorof Violin[D].Guangzhou:South China University of Technology,2014.
[23]TAKAGI T,SUGENO M.Fuzzy identification of systems and its applications to modeling and control[J].IEEE Trans.Syst.Man Cybern,1985(1):116-132.
[24]SUN D,LIAO Q,LOUTFI A.Type-2 fuzzy model-based movement primitives for imitation learning[J].IEEE Transactions on Robotics,2022,38(4):2462-2480.
[25]SHLENS J.A tutorial on principal component analysis[J].ar-Xiv:1404.1100,2014.
[26]GUO D L.Theory and Application of Fuzzy Systems [M].Beijing:Science Press,2021.
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