mlsanityData to 7 Oct 2026

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

arXiv 2604.22102 ↗First posted 23 Apr 2026cs.RO cs.AI cs.LGcited by 0

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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/

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 40 references that are in the map. First posted on arXiv 23 Apr 2026.

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References · 28

  1. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,490
  2. 2024Grounded SAM: Assembling Open-World Models for Diverse Visual Taskscited by 914
  3. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 430
  4. 2018Sim-to-Real Reinforcement Learning for Deformable Object Manipulationcited by 124
  5. 2020SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulationcited by 118
  6. 2022In-Hand Object Rotation via Rapid Motor Adaptationcited by 102
  7. 2021gradSim: Differentiable simulation for system identification and visuomotor controlcited by 100
  8. 2018ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Roboticscited by 98
  9. 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
  10. 2023Multi-Stage Cable Routing through Hierarchical Imitation Learningcited by 59
  11. 2022Iterative Residual Policy: for Goal-Conditioned Dynamic Manipulation of Deformable Objectscited by 36
  12. 2024DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulationcited by 24
16 more
  1. 2020Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cablescited by 21
  2. 2022Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networkscited by 17
  3. 2022DROPO: Sim-to-Real Transfer with Offline Domain Randomizationcited by 16
  4. 2023Rapid Motor Adaptation for Robotic Manipulator Armscited by 13
  5. 2024Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objectscited by 9
  6. 2022Efficiently Learning Single-Arm Fling Motions to Smooth Garmentscited by 8
  7. 2025Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learningcited by 6
  8. 2021Shape Control of Deformable Linear Objects with Offline and Online Learning of Local Linear Deformation Modelscited by 4
  9. 2025Agile and Cooperative Aerial Manipulation of a Cable-Suspended Loadcited by 4
  10. 2023GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policycited by 3
  11. 2024Self-Supervised Learning of Dynamic Planar Manipulation of Free-End Cablescited by 3
  12. 2023Nonlinear MPC for Full-Pose Manipulation of a Cable-Suspended Load using Multiple UAVscited by 2
  13. 2024Optimal Cosserat-based deformation control for robotic manipulation of linear objectscited by 2
  14. 2023GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policycited by 1
  15. 2026SCOPE: Smooth Convex Optimization for Planned Evolution of Deformable Linear Objectscited by 1
  16. 2026Learning Dynamic Rope Manipulation Using Task-Level Iterative Learning Controlcited by 1

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