A Data-Centric Approach For Dual-Arm Robotic Garment Flattening

TL;DR

This paper introduces a data-centric method using KCNet to recognize garment configurations from depth images, enabling a dual-arm robot to efficiently flatten garments with high accuracy and versatility.

Contribution

The paper presents a novel KCNet-based approach for recognizing garment configurations, improving the efficiency and accuracy of robotic garment flattening.

Findings

92% accuracy in recognizing hanging garment configurations

Successful flattening of five different garment shapes

Average operation time of 221.6 seconds per garment

Abstract

Due to the high dimensionality of object states, a garment flattening pipeline requires recognising the configurations of garments for a robot to produce/select manipulation plans to flatten garments. In this paper, we propose a data-centric approach to identify known configurations of garments based on a known configuration network (KCNet) trained on depth images that capture the known configurations of garments and prior knowledge of garment shapes. In this paper, we propose a data-centric approach to identify the known configurations of garments based on a known configuration network (KCNet) trained on the depth images that capture the known configurations of garments and prior knowledge of garment shapes. The known configurations of garments are the configurations of garments when a robot hangs garments in the middle of the air. We found that it is possible to achieve 92\% accuracy if we let the robot recognise the common hanging configurations (the known configurations) of garments. We also demonstrate an effective robot garment flattening pipeline with our proposed approach on a dual-arm Baxter robot. The robot achieved an average operating time of 221.6 seconds and successfully manipulated garments of five different shapes.