Keypoint-Based Planar Bimanual Shaping of Deformable Linear Objects under Environmental Constraints with Hierarchical Action Framework

Shengzeng Huo, Anqing Duan, Chengxi Li, Peng Zhou, Wanyu Ma, Hesheng Wang, David Navarro-Alarcon

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

This letter addresses the problem of contact-based manipulation of deformable linear objects (DLOs) towards desired shapes with a dual-arm robotic system. To alleviate the burden of high-dimensional continuous state-action spaces, we model DLOs as kinematic multibody systems via our proposed keypoint encoding network. This novel encoding is trained on a synthetic labeled image dataset without requiring any manual annotations and can be directly transferred to real manipulation scenarios.Our goal-conditioned policy efficiently rearranges the configuration of the DLO based on the keypoints. The proposed hierarchical action framework tackles the manipulation problem in a coarse-to-fine manner (with high-level task planning and low-level motion control) by leveraging two action primitives. The identification of deformation properties is bypassed since the algorithm replans its motion after each bimanual execution. The conducted experimental results reveal that our method achieves high performance in state representation and shaping manipulation of the DLO under environmental constraints.

Original languageEnglish
Pages (from-to)5222-5229
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number2
DOIs
Publication statusPublished - 1 Apr 2022

Keywords

  • Action planning
  • deformable linear objects
  • hierarchical framework
  • robot manipulation
  • synthetic learning

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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