# Deformable Elasto-Plastic Object Shaping using an Elastic Hand and Model-Based Reinforcement Learning

arXiv: https://arxiv.org/abs/2107.06924 · HTML: https://mlsanity.com/paper/2107.06924/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2021.

## Built on by (most cited first, 7 of 7)

- [RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks](https://mlsanity.com/paper/2205.02909/) (2022)
- [DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics](https://mlsanity.com/paper/2304.03223/) (2023)
- [RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing](https://mlsanity.com/paper/2407.01418/) (2024)
- [DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects](https://mlsanity.com/paper/2404.12524/) (2024)
- [Robotic Dough Shaping](https://mlsanity.com/paper/2208.00386/) (2022)
- [Towards Improving Learning from Demonstration Algorithms via MCMC Methods](https://mlsanity.com/paper/2405.02243/) (2024)
- [Optimal Gait Control for a Tendon-driven Soft Quadruped Robot by Model-based Reinforcement Learning](https://mlsanity.com/paper/2406.07069/) (2024)

## Abstract (arXiv)

Deformable solid objects such as clay or dough are prevalent in industrial and home environments. However, robotic manipulation of such objects has largely remained unexplored in literature due to the high complexity involved in representing and modeling their deformation. This work addresses the problem of shaping elasto-plastic dough by proposing to use a novel elastic end-effector to roll dough in a reinforcement learning framework. The transition model for the end-effector-to-dough interactions is learned from one hour of robot exploration, and doughs of different hydration levels are rolled out into varying lengths. Experimental results are encouraging, with the proposed framework accomplishing the task of rolling out dough into a specified length with 60% fewer actions than a heuristic method. Furthermore, we show that estimating stiffness using the soft end-effector can be used to effectively initialize models, improving robot performance by approximately 40% over incorrect model initialization.