Coupled Multiple Dynamic Movement Primitives Generalization for Deformable Object Manipulation

Zhenxi Cui, Wanyu Ma, Jiewen Lai, Henry K. Chu, Yi Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


Dynamic Movement Primitives (DMP) are widely applied in movement representation due to their ability to encode tasks using generalization properties. However, the coupled multiple DMP generalization cannot be directly solved based on the original DMP formula. Prior works provide satisfactory performance for the coupled DMP generalization in rigid object manipulation, but their extension to deformable objects may degrade due to the intrinsic uncertainty of the deformable model structure and parameters. This letter introduces an adaptive term to replace the fixed term to couple multiple DMP generalizations and model the deformable object using the classic mass-spring-damper model. Based on the modeling, the manipulation of a deformable object can be treated as a second-order system, which provides additional implementation flexibility and robustness in deformable object transportation. To validate the proposed method, we perform extensive simulations for cooperatively transporting a rope and a deformable thin film, imitating the manipulation with a semi-ellipse trajectory and M-shape trajectory. We further implement our method on a dual-arm robot platform for rope manipulation with depth visual feedback. Both simulation and experiment results demonstrate satisfactory DMP generalization, collision avoidance, and configuration preservation.

Original languageEnglish
Pages (from-to)5381-5388
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - 1 Apr 2022


  • robot trajectory planning and control
  • Soft object manipulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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