# Recognising Known Configurations of Garments For Dual-Arm Robotic Flattening

arXiv: https://arxiv.org/abs/2205.00225 · HTML: https://mlsanity.com/paper/2205.00225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 30 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [Learning Predictive Representations for Deformable Objects Using Contrastive Estimation](https://mlsanity.com/paper/2003.05436/) (2020)
- [Folding Deformable Objects using Predictive Simulation and Trajectory Optimization](https://mlsanity.com/paper/1512.06922/) (2015)
- [Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects](https://mlsanity.com/paper/1607.04411/) (2016)
- [Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces](https://mlsanity.com/paper/2001.11051/) (2020)
- [GarNet: A Continuous Robot Vision Approach for Predicting Shapes and Visually Perceived Weights of Garments](https://mlsanity.com/paper/2109.07831/) (2021)
- [Learning Physics Properties of Fabrics and Garments with a Physics Similarity Neural Network](https://mlsanity.com/paper/2112.10727/) (2021)

## Abstract (arXiv)

Robotic deformable-object manipulation is a challenge in the robotic industry because deformable objects have complicated and various object states. Predicting those object states and updating manipulation planning is time-consuming and computationally expensive. In this paper, we propose learning known configurations of garments to allow a robot to recognise garment states and choose a pre-designed manipulation plan for garment flattening.