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This paper addresses the problem of contact-based manipulation of deformablelinear objects (DLOs) towards desired shapes with a dual-arm robotic system. Toalleviate the burden of high-dimensional continuous state-action spaces, wemodel the DLO as a kinematic multibody system via our proposed keypointdetection network. This new perception network is trained on a syntheticlabeled image dataset and transferred to real manipulation scenarios withoutconducting any manual annotations. Our goal-conditioned policy can efficientlylearn to rearrange the configuration of the DLO based on the detectedkeypoints. The proposed hierarchical action framework tackles the manipulationproblem in a coarse-to-fine manner (with high-level task planning and low-levelmotion control) by leveraging on two action primitives. The identification ofdeformation properties is avoided since the algorithm replans its motion aftereach bimanual execution. The conducted experimental results reveal that ourmethod achieves high performance in state representation of the DLO, and isrobust to uncertain environmental constraints.

Keypoint-Based Bimanual Shaping of Deformable Linear Objects under Environmental Constraints using Hierarchical Action Planning