Tidy-Up Tasks Using Trajectory-based Imitation Learning

Doo Jun Kim, Hyun Jun Jo, Jae Bok Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution


When performing reinforcement learning using a robot arm in the real environment, it is important to perform reinforcement learning safely and quickly. This is because unexpected behaviors during reinforcement learning and long-term learning can damage the robot arm or surrounding objects. In this study, trajectory-based imitation learning that suppresses unexpected situations and quickly learns the policies suitable for the robots is proposed by limiting the workspace to be explored through one human demonstration. Trajectory-based imitation learning consists of two stages. First, a reference trajectory is generated considering the position of a target object and the expert trajectory from the human demonstration. Second, the target task is trained by performing reinforcement learning based on the generated reference trajectory. Experiments were conducted in simulation and real environments to verify the proposed imitation learning algorithm. In the simulation, a laptop folding task was performed with a success rate of 97% to verify the performance of the algorithm. In addition, it was shown that safe and fast learning is possible with only one demonstration video on the drawer arrangement in a real environment.

Original languageEnglish
Title of host publication2021 21st International Conference on Control, Automation and Systems, ICCAS 2021
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9788993215212
Publication statusPublished - 2021
Event21st International Conference on Control, Automation and Systems, ICCAS 2021 - Jeju, Korea, Republic of
Duration: 2021 Oct 122021 Oct 15

Publication series

NameInternational Conference on Control, Automation and Systems
ISSN (Print)1598-7833


Conference21st International Conference on Control, Automation and Systems, ICCAS 2021
Country/TerritoryKorea, Republic of

Bibliographical note

Funding Information:
This work was supported by IITP grant funded by the Korea Government MSIT. (No. 2018-0-00622)

Publisher Copyright:
© 2021 ICROS.


  • Human demonstration
  • Manipulation
  • Reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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