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

TL;DR

The paper introduces Wiggle and Go!, a zero-shot dynamic rope manipulation system that uses learned simulation priors and system identification to achieve accurate, task-agnostic control without real-world training data.

Contribution

It presents a novel two-stage framework combining system identification and goal-conditioned optimization for zero-shot dynamic rope manipulation.

Findings

Achieves 3.55 cm accuracy on 3D target striking in real-world tests.

High correlation (0.95) between predicted and real rope Fourier frequencies.

Supports multiple manipulation tasks with a single, task-agnostic model.

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/