Recognising Known Configurations of Garments For Dual-Arm Robotic Flattening
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 30 Apr 2022.
Builds on
9 of 21 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2018Sim-to-Real Reinforcement Learning for Deformable Object Manipulationcited by 124
- 2020Learning Predictive Representations for Deformable Objects Using Contrastive Estimationcited by 72
- 2015Folding Deformable Objects using Predictive Simulation and Trajectory Optimizationcited by 19
- 2016Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objectscited by 6
- 2020Learning When to Trust a Dynamics Model for Planning in Reduced State Spacescited by 3
- 2021GarNet: A Continuous Robot Vision Approach for Predicting Shapes and Visually Perceived Weights of Garmentscited by 2
- 2021Learning Physics Properties of Fabrics and Garments with a Physics Similarity Neural Networkcited by 1
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