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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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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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.