# Robotic Dough Shaping

arXiv: https://arxiv.org/abs/2208.00386 · HTML: https://mlsanity.com/paper/2208.00386/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [DiffTaichi: Differentiable Programming for Physical Simulation](https://mlsanity.com/paper/1910.00935/) (2019)
- [Differentiable Surface Splatting for Point-based Geometry Processing](https://mlsanity.com/paper/1906.04173/) (2019)
- [Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion](https://mlsanity.com/paper/2111.12702/) (2021)
- [PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics](https://mlsanity.com/paper/2104.03311/) (2021)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1907.13098/) (2019)
- [FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding](https://mlsanity.com/paper/2105.03655/) (2021)
- [Learning Predictive Representations for Deformable Objects Using Contrastive Estimation](https://mlsanity.com/paper/2003.05436/) (2020)
- [Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data](https://mlsanity.com/paper/2003.01835/) (2020)
- [Iterative Residual Policy: for Goal-Conditioned Dynamic Manipulation of Deformable Objects](https://mlsanity.com/paper/2203.00663/) (2022)
- [Manipulating Deformable Objects by Interleaving Prediction, Planning, and Control](https://mlsanity.com/paper/2001.09950/) (2020)
- [Deformable Elasto-Plastic Object Shaping using an Elastic Hand and Model-Based Reinforcement Learning](https://mlsanity.com/paper/2107.06924/) (2021)
- [Vision-based Manipulation of Deformable and Rigid Objects Using Subspace Projections of 2D Contours](https://mlsanity.com/paper/2006.09023/) (2020)

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

- [PinchBot: Long-Horizon Deformable Manipulation with Guided Diffusion Policy](https://mlsanity.com/paper/2507.17846/) (2025)

## Abstract (arXiv)

Robotic manipulation of deformable objects gains great attention due to its wide applications including medical surgery, home assistance, and automatic food preparation. The ability to deform soft objects remains a great challenge for robots due to difficulties in defining the problem mathematically. In this paper, we address the problem of shaping a piece of dough-like deformable material into a 2D target shape presented upfront. We use a 6 degree-of-freedom WidowX-250 Robot Arm equipped with a rolling pin and information collected from an RGB-D camera and a tactile sensor. We present and compare several control policies, including a dough shrinking action, in extensive experiments across three kinds of deformable materials and across three target dough shape sizes, achieving the intersection over union (IoU) of 0.90. Our results show that: i) rolling dough from the highest dough point is more efficient than from the 2D/3D dough centroid; ii) it might be better to stop the roll movement at the current dough boundary as opposed to the target shape outline; iii) the shrink action might be beneficial only if properly tuned with respect to the expand action; and iv) the Play-Doh material is easier to shape to a target shape as compared to Plasticine or Kinetic sand. Video demonstrations of our work are available at https://youtu.be/ZzLMxuITdt4