Robotic Dough Shaping
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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
Key facts. Cited by 1 paper 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
13 of 33 references.
References · 13
- 2019DiffTaichi: Differentiable Programming for Physical Simulationcited by 258
- 2019Differentiable Surface Splatting for Point-based Geometry Processingcited by 239
- 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 112
- 2021PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physicscited by 103
- 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
- 2019Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Taskscited by 84
- 2021FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfoldingcited by 81
- 2020Learning Predictive Representations for Deformable Objects Using Contrastive Estimationcited by 72
- 2020Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Datacited by 51
- 2022Iterative Residual Policy: for Goal-Conditioned Dynamic Manipulation of Deformable Objectscited by 37
- 2020Manipulating Deformable Objects by Interleaving Prediction, Planning, and Controlcited by 9
- 2021Deformable Elasto-Plastic Object Shaping using an Elastic Hand and Model-Based Reinforcement Learningcited by 7
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1 paper.