# GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy

arXiv: https://arxiv.org/abs/2306.09872 · HTML: https://mlsanity.com/paper/2306.09872/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 49 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 30 of 33)

- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [DiffTaichi: Differentiable Programming for Physical Simulation](https://mlsanity.com/paper/1910.00935/) (2019)
- [One-Shot Visual Imitation Learning via Meta-Learning](https://mlsanity.com/paper/1709.04905/) (2017)
- [Learning Fine-grained Image Similarity with Deep Ranking](https://mlsanity.com/paper/1404.4661/) (2014)
- [Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks](https://mlsanity.com/paper/1812.07252/) (2018)
- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (2019)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics](https://mlsanity.com/paper/2104.03311/) (2021)
- [gradSim: Differentiable simulation for system identification and visuomotor control](https://mlsanity.com/paper/2104.02646/) (2021)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting](https://mlsanity.com/paper/2105.12244/) (2021)
- [Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data](https://mlsanity.com/paper/2003.01835/) (2020)
- [One-Shot Generalization in Deep Generative Models](https://mlsanity.com/paper/1603.05106/) (2016)
- [Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects](https://mlsanity.com/paper/1911.06283/) (2019)
- [Differentiable Physics Simulation of Dynamics-Augmented Neural Objects](https://mlsanity.com/paper/2210.09420/) (2022)
- [Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real](https://mlsanity.com/paper/1908.05224/) (2019)
- [Learning Robotic Manipulation through Visual Planning and Acting](https://mlsanity.com/paper/1905.04411/) (2019)
- [Planar Robot Casting with Real2Sim2Real Self-Supervised Learning](https://mlsanity.com/paper/2111.04814/) (2021)
- [i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops](https://mlsanity.com/paper/2207.06572/) (2022)
- [DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects](https://mlsanity.com/paper/2204.03139/) (2022)
- [RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation](https://mlsanity.com/paper/2205.05678/) (2022)
- [DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics](https://mlsanity.com/paper/2210.13066/) (2022)
- [A Real2Sim2Real Method for Robust Object Grasping with Neural Surface Reconstruction](https://mlsanity.com/paper/2210.02685/) (2022)
- [Learning to Augment Synthetic Images for Sim2Real Policy Transfer](https://mlsanity.com/paper/1903.07740/) (2019)
- [A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation](https://mlsanity.com/paper/2112.05068/) (2021)
- [Learning Closed-loop Dough Manipulation Using a Differentiable Reset Module](https://mlsanity.com/paper/2207.04638/) (2022)
- [Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics](https://mlsanity.com/paper/2303.01310/) (2023)
- [Learning to Brachiate via Simplified Model Imitation](https://mlsanity.com/paper/2205.03943/) (2022)
- [Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging](https://mlsanity.com/paper/2302.10446/) (2023)
- [In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning](https://mlsanity.com/paper/2103.09402/) (2021)

## Built on by (most cited first, 1 of 1)

- [Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation](https://mlsanity.com/paper/2604.22102/) (2026)

## Abstract (arXiv)

Due to the inherent uncertainty in their deformability during motion, previous methods in rope manipulation often require hundreds of real-world demonstrations to train a manipulation policy for each rope, even for simple tasks such as rope goal reaching, which hinder their applications in our ever-changing world. To address this issue, we introduce GenORM, a framework that allows the manipulation policy to handle different deformable ropes with a single real-world demonstration. To achieve this, we augment the policy by conditioning it on deformable rope parameters and training it with a diverse range of simulated deformable ropes so that the policy can adjust actions based on different rope parameters. At the time of inference, given a new rope, GenORM estimates the deformable rope parameters by minimizing the disparity between the grid density of point clouds of real-world demonstrations and simulations. With the help of a differentiable physics simulator, we require only a single real-world demonstration. Empirical validations on both simulated and real-world rope manipulation setups clearly show that our method can manipulate different ropes with a single demonstration and significantly outperforms the baseline in both environments (62% improvement in in-domain ropes, and 15% improvement in out-of-distribution ropes in simulation, 26% improvement in real-world), demonstrating the effectiveness of our approach in one-shot rope manipulation.