# VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation

arXiv: https://arxiv.org/abs/2003.09044 · HTML: https://mlsanity.com/paper/2003.09044/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2020.

## Built on by (most cited first, 30 of 40)

- [Learning Visible Connectivity Dynamics for Cloth Smoothing](https://mlsanity.com/paper/2105.10389/) (2021)
- [FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy](https://mlsanity.com/paper/2111.05623/) (2021)
- [AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation](https://mlsanity.com/paper/2407.07889/) (2024)
- [SpeedFolding: Learning Efficient Bimanual Folding of Garments](https://mlsanity.com/paper/2208.10552/) (2022)
- [LazyDAgger: Reducing Context Switching in Interactive Imitation Learning](https://mlsanity.com/paper/2104.00053/) (2021)
- [Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation](https://mlsanity.com/paper/2206.02881/) (2022)
- [Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos](https://mlsanity.com/paper/2506.15680/) (2025)
- [Visuotactile Affordances for Cloth Manipulation with Local Control](https://mlsanity.com/paper/2212.05108/) (2022)
- [VisuoSpatial Foresight for Physical Sequential Fabric Manipulation](https://mlsanity.com/paper/2102.09754/) (2021)
- [SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo](https://mlsanity.com/paper/2106.16118/) (2021)
- [UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding](https://mlsanity.com/paper/2311.01267/) (2023)
- [Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies](https://mlsanity.com/paper/2107.08942/) (2021)
- [LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks](https://mlsanity.com/paper/2107.04775/) (2021)
- [Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention](https://mlsanity.com/paper/2301.03003/) (2023)
- [Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks](https://mlsanity.com/paper/2202.10765/) (2022)
- [Autonomously Untangling Long Cables](https://mlsanity.com/paper/2207.07813/) (2022)
- [Multi-Object Manipulation via Object-Centric Neural Scattering Functions](https://mlsanity.com/paper/2306.08748/) (2023)
- [In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning](https://mlsanity.com/paper/2103.09402/) (2021)
- [Disentangling Dense Multi-Cable Knots](https://mlsanity.com/paper/2106.02252/) (2021)
- [Learning Fabric Manipulation in the Real World with Human Videos](https://mlsanity.com/paper/2211.02832/) (2022)
- [Visual Perspective Taking for Opponent Behavior Modeling](https://mlsanity.com/paper/2105.05145/) (2021)
- [Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research](https://mlsanity.com/paper/2204.10297/) (2022)
- [SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception](https://mlsanity.com/paper/2209.13706/) (2022)
- [Tell Me What's Next: Textual Foresight for Generic UI Representations](https://mlsanity.com/paper/2406.07822/) (2024)
- [InstructionBench: An Instructional Video Understanding Benchmark](https://mlsanity.com/paper/2504.05040/) (2025)
- [Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap](https://mlsanity.com/paper/2103.02554/) (2021)
- [Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance](https://mlsanity.com/paper/2509.03889/) (2025)
- [Semantic State Estimation in Cloth Manipulation Tasks](https://mlsanity.com/paper/2203.11647/) (2022)
- [All You Need is LUV: Unsupervised Collection of Labeled Images using Invisible UV Fluorescent Indicators](https://mlsanity.com/paper/2203.04566/) (2022)
- [Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation](https://mlsanity.com/paper/2207.01840/) (2022)

## Abstract (arXiv)

Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but related tasks. We extend the Visual Foresight framework to learn fabric dynamics that can be efficiently reused to accomplish different fabric manipulation tasks with a single goal-conditioned policy. We introduce VisuoSpatial Foresight (VSF), which builds on prior work by learning visual dynamics on domain randomized RGB images and depth maps simultaneously and completely in simulation. We experimentally evaluate VSF on multi-step fabric smoothing and folding tasks against 5 baseline methods in simulation and on the da Vinci Research Kit (dVRK) surgical robot without any demonstrations at train or test time. Furthermore, we find that leveraging depth significantly improves performance. RGBD data yields an 80% improvement in fabric folding success rate over pure RGB data. Code, data, videos, and supplementary material are available at https://sites.google.com/view/fabric-vsf/.