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Survey of imitation learning for robotic manipulation

  • Survey paper
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International Journal of Intelligent Robotics and Applications Aims and scope Submit manuscript

Abstract

With the development of robotics, the application of robots has gradually evolved from industrial scenes to more intelligent service scenarios. For multitasking operations of robots in complex and uncertain environments, the traditional manual coding method is not only cumbersome but also unable to adapt to sudden changes in the environment. Imitation learning that avoids learning skills from scratch by using the expert demonstration has become the most effective way for robotic manipulation. The paper is intended to provide the survey of imitation learning of robotic manipulation and explore the future research trend. The review of the art of imitation learning for robotic manipulation involves three aspects that are demonstration, representation and learning algorithms. Towards the end of the paper, we highlight areas of future research potential.

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Acknowledgements

This work is jointly supported by Foshan-Tsinghua industry-university-research cooperation collaborative innovation special fund no. 2018THFS04, Tsinghua University Initiative Scientific Research Program no. 2019Z08QCX15, National Natural Science Foundation of China under with Grant nos. 91848206, U1613212 and 61703284.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology, The Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing, 100084, People’s Republic of China

    Bin Fang, Di Guo & Fuchun Sun

  2. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, People’s Republic of China

    Shidong Jia & Muhua Xu

  3. Princeton International School of Mathematics and Science, 19 Lambert Drive, Princeton, NJ, 08540, USA

    Shuhuan Wen

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  1. Bin Fang
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  2. Shidong Jia
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  3. Di Guo
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  4. Muhua Xu
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  5. Shuhuan Wen
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  6. Fuchun Sun
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Correspondence to Bin Fang.

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Fang, B., Jia, S., Guo, D. et al. Survey of imitation learning for robotic manipulation. Int J Intell Robot Appl 3, 362–369 (2019). https://doi.org/10.1007/s41315-019-00103-5

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