  This paper explores the development of UniFolding, a sample-efficient,
scalable, and generalizable robotic system for unfolding and folding various
garments. UniFolding employs the proposed UFONet neural network to integrate
unfolding and folding decisions into a single policy model that is adaptable to
different garment types and states. The design of UniFolding is based on a
garment's partial point cloud, which aids in generalization and reduces
sensitivity to variations in texture and shape. The training pipeline
prioritizes low-cost, sample-efficient data collection. Training data is
collected via a human-centric process with offline and online stages. The
offline stage involves human unfolding and folding actions via Virtual Reality,
while the online stage utilizes human-in-the-loop learning to fine-tune the
model in a real-world setting. The system is tested on two garment types:
long-sleeve and short-sleeve shirts. Performance is evaluated on 20 shirts with
significant variations in textures, shapes, and materials. More experiments and
videos can be found in the supplementary materials and on the website:
https://unifolding.robotflow.ai
