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

arXiv cs.AI / 4/27/2026

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Key Points

The paper proposes “Wiggle and Go!”, a two-stage system-identification framework for zero-shot dynamic rope manipulation in real robotic tasks.
Instead of needing large real-world rope datasets or trial-and-error task iterations, the approach learns simulation priors and uses observed rope motion to predict descriptive physical parameters.
Those inferred system parameters then condition an optimization method that generates goal-conditioned robot actions to perform tasks without retraining for each new goal.
The method supports multiple dynamic rope manipulation tasks by using a single task-agnostic system-identification module, enabling seamless switching between policies.
Experiments report improved accuracy for 3D target striking in the real world (3.55 cm vs. 15.34 cm without parameter-informed task modeling) and high agreement in rope dynamics (Pearson correlation 0.95 for Fourier frequency spectra on an unseen trajectory).

Abstract

Many robotic tasks are unforgiving; a single mistake in a dynamic throw can lead to unacceptable delays or unrecoverable failure. To mitigate this, we present a novel approach that leverages learned simulation priors to inform goal-conditioned dynamic manipulation of ropes for efficient and accurate task execution. Related methods for dynamic rope manipulation either require large real-world datasets to estimate rope behavior or the use of iterative improvements on attempts at the task for goal completion. We introduce Wiggle and Go!, a system-identification, two-stage framework that enables zero-shot task rope manipulation. The framework consists of a system identification module that observes rope movement to predict descriptive physical parameters, which then informs an optimization method for goal-conditioned action prediction for the robot to execute zero-shot in the real. Our method achieves strong performance across multiple dynamic manipulation tasks enabled by the same task-agnostic system identification module which offers seamless switching between different manipulation tasks, allowing a single model to support a diverse array of manipulation policies. We achieve a 3.55 cm average accuracy on 3D target striking in real using rope system parameters in comparison to 15.34 cm accuracy when our task model is not system-parameter-informed. We achieve a Pearson correlation coefficient of 0.95 between Fourier frequencies of the predicted and real ropes on an unseen trajectory. Project website please see https://wiggleandgo.github.io/