Task-level ILC learns flying knot rope manipulation from one demo, achieving 100% success within 10 trials on 7 rope types with 2-5 trial transfers.
Iterative residual policy: for goal-conditioned dynamic manipulation of deformable objects
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.RO 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
DeformX integrates Cosserat rod physics with Isaac Sim for DLO simulation, enabling data generation that improves real-image segmentation by 10.2% mAP@75 and a rope policy transferred to a UR5e with 6.6 cm error.
Wiggle and Go! uses system identification from rope motion observations to predict parameters that enable zero-shot goal-conditioned dynamic manipulation, achieving 3.55 cm accuracy on 3D target striking versus 15.34 cm without parameter information.
citing papers explorer
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Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Control
Task-level ILC learns flying knot rope manipulation from one demo, achieving 100% success within 10 trials on 7 rope types with 2-5 trial transfers.
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DeformX: A Versatile Co-Simulation Framework for Deformable Linear Objects
DeformX integrates Cosserat rod physics with Isaac Sim for DLO simulation, enabling data generation that improves real-image segmentation by 10.2% mAP@75 and a rope policy transferred to a UR5e with 6.6 cm error.
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Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation
Wiggle and Go! uses system identification from rope motion observations to predict parameters that enable zero-shot goal-conditioned dynamic manipulation, achieving 3.55 cm accuracy on 3D target striking versus 15.34 cm without parameter information.