# PinchBot: Long-Horizon Deformable Manipulation with Guided Diffusion Policy

arXiv: https://arxiv.org/abs/2507.17846 · HTML: https://mlsanity.com/paper/2507.17846/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 26 of its 30 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 26 of 26)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling](https://mlsanity.com/paper/2111.14819/) (2021)
- [Steering Your Diffusion Policy with Latent Space Reinforcement Learning](https://mlsanity.com/paper/2506.15799/) (2025)
- [RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools](https://mlsanity.com/paper/2306.14447/) (2023)
- [RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks](https://mlsanity.com/paper/2205.02909/) (2022)
- [Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning](https://mlsanity.com/paper/2402.02500/) (2024)
- [DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics](https://mlsanity.com/paper/2304.03223/) (2023)
- [Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation](https://mlsanity.com/paper/2503.11999/) (2025)
- [Learning Closed-loop Dough Manipulation Using a Differentiable Reset Module](https://mlsanity.com/paper/2207.04638/) (2022)
- [Differentiable Particles for General-Purpose Deformable Object Manipulation](https://mlsanity.com/paper/2405.01044/) (2024)
- [DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects](https://mlsanity.com/paper/2404.12524/) (2024)
- [SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation](https://mlsanity.com/paper/2309.08728/) (2023)
- [SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy](https://mlsanity.com/paper/2403.10401/) (2024)
- [LLM-Craft: Robotic Crafting of Elasto-Plastic Objects with Large Language Models](https://mlsanity.com/paper/2406.08648/) (2024)
- [SSFold: Learning to Fold Arbitrary Crumpled Cloth Using Graph Dynamics from Human Demonstration](https://mlsanity.com/paper/2411.02608/) (2024)
- [General-purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2408.08160/) (2024)
- [DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects](https://mlsanity.com/paper/2312.15204/) (2023)
- [RoPotter: Toward Robotic Pottery and Deformable Object Manipulation with Structural Priors](https://mlsanity.com/paper/2408.02184/) (2024)
- [Planning and Reasoning with 3D Deformable Objects for Hierarchical Text-to-3D Robotic Shaping](https://mlsanity.com/paper/2412.01765/) (2024)
- [Manipulating Elasto-Plastic Objects With 3D Occupancy and Learning-Based Predictive Control](https://mlsanity.com/paper/2505.16249/) (2025)
- [Robotic Dough Shaping](https://mlsanity.com/paper/2208.00386/) (2022)
- [Make a Donut: Hierarchical EMD-Space Planning for Zero-Shot Deformable Manipulation with Tools](https://mlsanity.com/paper/2311.02787/) (2023)

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

- [RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning](https://mlsanity.com/paper/2510.14830/) (2025)

## Abstract (arXiv)

Pottery creation is a complicated art form that requires dexterous, precise and delicate actions to slowly morph a block of clay to a meaningful, and often useful 3D goal shape. In this work, we aim to create a robotic system that can create simple pottery goals with only pinch-based actions. This pinch pottery task allows us to explore the challenges of a highly multi-modal and long-horizon deformable manipulation task. To this end, we present PinchBot, a goal-conditioned diffusion policy model that when combined with pre-trained 3D point cloud embeddings, task progress prediction and collision-constrained action projection, is able to successfully create a variety of simple pottery goals. For experimental videos and access to the demonstration dataset, please visit our project website: https://sites.google.com/andrew.cmu.edu/pinchbot/home.