UniGarmentManip: A Unified Framework for Category-Level GarmentManipulation via Dense Visual Correspondence
Garment manipulation (e.g., unfolding, folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks, while highly challenging due to the diversity of garment configurations, geometries and deformations.
Although able to manipulate similar shaped garments in a certain task,
previous works mostly have to design different policies for different tasks, could not generalize to garments with diverse geometries, and often rely heavily on human-annotated data.
In this paper, we leverage the property that,
garments in a certain category have similar structures,
and then learn the topological dense (point-level) visual correspondence among garments in the category level with different deformations in the self-supervised manner.
The topological correspondence can be easily adapted to the functional correspondence to guide the manipulation policies for various downstream tasks,
within only one or few-shot demonstrations.
Experiments over garments in 3 different categories on 3 representative tasks in diverse scenarios,
using one or two arms,
taking one or more steps,
inputting flat or messy garments,
demonstrate the effectiveness of our proposed method.
This repository provides data and code as follows.
garmentgym/ # The garment manipulation simulator
skeleton/ # The skeleton code for garment structure learning
task/ # The code for garment manipulation tasks
train/ # The code for training dense visual correspondence
collect/ # The code for collecting data
demonstration/ # The code for one-shot demonstration
You can follow the README in EACH FOLDER to install and run the code.