arXiv Machine Learning September 11, 2026 By Arthur Jakobsson, Abhinav Mahajan, Karthik Pullalarevu, Krishna Suresh, Yunchao Yao, Yuemin Mao, Bardienus Duisterhof, Shahram Najam Syed, Jeffrey Ichnowski

Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation

Read the original on arXiv Machine Learning →

Wiggle and Go! is a two‑stage framework for zero‑shot rope manipulation that first performs a brief, safe wiggle action to infer rope parameters, then uses those parameters to condition a trajectory optimizer for goal‑conditioned execution. The method achieves 3.55 cm average accuracy on 3D target striking in real‑world tests, far outperforming uninformed baselines, and secures over 50% success on multi‑objective lobbing and draping tasks. Predicted parameters transfer well to unseen motions, with a 0.95 Pearson correlation between simulated and real rope dynamics, demonstrating task‑agnostic generalization without retraining.

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