Automated Action Generation based on Action Field for Robotic Garment Smoothing and Alignment

TL;DR

This paper introduces a neural network-based method for robotic garment smoothing and alignment that enhances accuracy, reduces computation time, and generalizes well to various garment types in both simulation and real-world settings.

Contribution

A novel action generator that interprets scene images to produce pixel-wise actions and predicts a manipulation score map for effective garment handling.

Findings

Achieves higher smoothing and alignment performance.

Reduces computational time compared to previous methods.

Successfully generalizes to different garment types in real-world experiments.

Abstract

Garment manipulation using robotic systems is a challenging task due to the diverse shapes and deformable nature of fabric. In this paper, we propose a novel method for robotic garment smoothing and alignment that significantly improves the accuracy while reducing computational time compared to previous approaches. Our method features an action generator that directly interprets scene images and generates pixel-wise end-effector action vectors using a neural network. The network also predicts a manipulation score map that ranks potential actions, allowing the system to select the most effective action. Extensive simulation experiments demonstrate that our method achieves higher smoothing and alignment performances and faster computation time than previous approaches. Real-world experiments show that the proposed method generalizes well to different garment types and successfully flattens garments.