# Iterative Residual Policy: for Goal-Conditioned Dynamic Manipulation of Deformable Objects

arXiv: https://arxiv.org/abs/2203.00663 · HTML: https://mlsanity.com/paper/2203.00663/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2022.

## Built on by (most cited first, 30 of 37)

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- [DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects](https://mlsanity.com/paper/2312.15204/) (2023)
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- [TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies](https://mlsanity.com/paper/2509.19712/) (2025)
- [Robotic Dough Shaping](https://mlsanity.com/paper/2208.00386/) (2022)
- [Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation](https://mlsanity.com/paper/2310.04930/) (2023)
- [Benchmarking Population-Based Reinforcement Learning across Robotic Tasks with GPU-Accelerated Simulation](https://mlsanity.com/paper/2404.03336/) (2024)
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- [A Distributional Treatment of Real2Sim2Real for Object-Centric Agent Adaptation in Vision-Driven Deformable Linear Object Manipulation](https://mlsanity.com/paper/2502.18615/) (2025)
- [Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation](https://mlsanity.com/paper/2505.16547/) (2025)

## Abstract (arXiv)

This paper tackles the task of goal-conditioned dynamic manipulation of deformable objects. This task is highly challenging due to its complex dynamics (introduced by object deformation and high-speed action) and strict task requirements (defined by a precise goal specification). To address these challenges, we present Iterative Residual Policy (IRP), a general learning framework applicable to repeatable tasks with complex dynamics. IRP learns an implicit policy via delta dynamics -- instead of modeling the entire dynamical system and inferring actions from that model, IRP learns delta dynamics that predict the effects of delta action on the previously-observed trajectory. When combined with adaptive action sampling, the system can quickly optimize its actions online to reach a specified goal. We demonstrate the effectiveness of IRP on two tasks: whipping a rope to hit a target point and swinging a cloth to reach a target pose. Despite being trained only in simulation on a fixed robot setup, IRP is able to efficiently generalize to noisy real-world dynamics, new objects with unseen physical properties, and even different robot hardware embodiments, demonstrating its excellent generalization capability relative to alternative approaches. Video is available at https://youtu.be/7h3SZ3La-oA