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

arXiv: https://arxiv.org/abs/2604.22102 · HTML: https://mlsanity.com/paper/2604.22102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 40 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Builds on (most cited first, 28 of 28)

- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/2011.07215/) (2020)
- [In-Hand Object Rotation via Rapid Motor Adaptation](https://mlsanity.com/paper/2210.04887/) (2022)
- [gradSim: Differentiable simulation for system identification and visuomotor control](https://mlsanity.com/paper/2104.02646/) (2021)
- [ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics](https://mlsanity.com/paper/1810.01054/) (2018)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [Multi-Stage Cable Routing through Hierarchical Imitation Learning](https://mlsanity.com/paper/2307.08927/) (2023)
- [Iterative Residual Policy: for Goal-Conditioned Dynamic Manipulation of Deformable Objects](https://mlsanity.com/paper/2203.00663/) (2022)
- [DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation](https://mlsanity.com/paper/2403.08716/) (2024)
- [Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables](https://mlsanity.com/paper/2011.04840/) (2020)
- [DROPO: Sim-to-Real Transfer with Offline Domain Randomization](https://mlsanity.com/paper/2201.08434/) (2022)
- [Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks](https://mlsanity.com/paper/2203.15004/) (2022)
- [Rapid Motor Adaptation for Robotic Manipulator Arms](https://mlsanity.com/paper/2312.04670/) (2023)
- [Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects](https://mlsanity.com/paper/2406.05931/) (2024)
- [Efficiently Learning Single-Arm Fling Motions to Smooth Garments](https://mlsanity.com/paper/2206.08921/) (2022)
- [Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning](https://mlsanity.com/paper/2505.14266/) (2025)
- [Agile and Cooperative Aerial Manipulation of a Cable-Suspended Load](https://mlsanity.com/paper/2501.18802/) (2025)
- [Shape Control of Deformable Linear Objects with Offline and Online Learning of Local Linear Deformation Models](https://mlsanity.com/paper/2109.11091/) (2021)
- [Nonlinear MPC for Full-Pose Manipulation of a Cable-Suspended Load using Multiple UAVs](https://mlsanity.com/paper/2301.08545/) (2023)
- [GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy](https://mlsanity.com/paper/2309.09051/) (2023)
- [Self-Supervised Learning of Dynamic Planar Manipulation of Free-End Cables](https://mlsanity.com/paper/2405.09581/) (2024)
- [Optimal Cosserat-based deformation control for robotic manipulation of linear objects](https://mlsanity.com/paper/2409.12723/) (2024)
- [Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Control](https://mlsanity.com/paper/2602.21302/) (2026)
- [GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy](https://mlsanity.com/paper/2306.09872/) (2023)
- [SCOPE: Smooth Convex Optimization for Planned Evolution of Deformable Linear Objects](https://mlsanity.com/paper/2601.19742/) (2026)

## Abstract (arXiv)

Many robotic tasks are unforgiving; a single mistake in a dynamic throw can lead to unacceptable delays or unrecoverable failure. We introduce Wiggle and Go!, a two-stage framework for zero-shot rope manipulation: a brief, safe wiggle action is observed to predict descriptive rope parameters, which then conditions a trajectory optimizer for zero-shot goal-conditioned execution. Unlike prior dynamic rope manipulation methods that require large real-world datasets or iterative real-world refinement, our identification module is task-agnostic, supporting diverse manipulation policies without retraining. We achieve a 3.55,cm average accuracy on 3D target striking in real using rope system parameters in comparison to 15.29,cm for uninformed baselines, and over 50% success on multi-objective lobbing and draping tasks. Predicted parameters transfer to unseen motions with 0.95 Pearson correlation between simulated and real rope dynamics, indicating that the identification module generalizes across the task corpus. Project website: https://wiggleandgo.github.io/