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.

Accurate fabric alignment is a critical step that must be performed before sewing. This paper presents a novel automated fabric alignment system. The system estimates the poses of top and bottom fabric panels—lying flat and wrinkle-free in arbitrary positions—using a new Global Local Weighted Iterative Closest Point (GLW-ICP) method. The system then manipulates the top panel to achieve precise alignment at both edges and sewing lines. Unlike conventional approaches, GLW-ICP robustly aligns both global edges and local sewing lines by globally aligning fabric edge points and locally aligning sewing line points to their corresponding CAD model points, while removing unmatched points in occluded regions. Real-world experiments with various fabric shapes show that the system consistently achieves millimeter-level alignment accuracy under both occlusion and non-occlusion conditions, demonstrating its effectiveness and suitability for automated fabric alignment in practical scenarios. Note to Practitioners—Fabric panel alignment before sewing is a time-consuming and skill-dependent task in garment production. Misaligned edges or sewing lines can lead to defects, rework, and production delays. This work presents a novel robotic system that automates the alignment of wrinkle-free fabric panels, even when portions of the panel are occluded by the manipulator. The system uses a novel Global Local Weighted Iterative Closest Point (GLW-ICP) method, which separately aligns overall panel edges and local sewing lines to a digital CAD model while ignoring unreliable points from occluded regions. A roller-based end-effector then picks up, re-positions, and releases the top panel to achieve precise alignment with the bottom panel. This method achieves millimeter-level accuracy across various garment components under both unoccluded and partially occluded views. This reduces operator dependency, improves consistency, and shortens preparation time. The approach is readily applicable to a wide range of garment components and can be adapted to various production settings. These capabilities open opportunities for end-to-end automation in apparel manufacturing, from panel preparation to stitching, further enhancing productivity and quality control.

This paper proposes a fixture-free 2D sewing system using a dual-arm manipulator, i.e., the seam lines of the top and bottom fabric parts are the same. The proposed 2D sewing system sews two stacked fabric parts together along a desired seam line printed on the top fabric part without the use of a fixture. In the proposed system, the set of aligned and stacked fabric parts is held by the end-effectors of the dual-arm manipulator in coordination. The dual-arm manipulator controls the motion of the fabric parts on the flat sewing table stitch by stitch in coordination, while keeping the manipulated fabric parts flat using the internal force applied to the set of fabric parts. A novel vision-based seam line tracking control is proposed to control the motion of the set of fabric parts along the printed seam line on the top fabric part. The convergence of the tracking error is analyzed for sewing along both straight and curved seam lines and is shown to be specified by the control parameters. Sewing experiments show that the tracking error converges to zero as analyzed. The sewing experiments also show that the newly proposed trajectory generation method, which synchronizes the coordinated motion of the manipulators and the motion of the sewing needle, is essential for achieving accurate sewing.Note to Practitioners—Most semi-automatic sewing machines and pattern sewers on the market use fixtures to handle the stacked fabric parts. They require the user to customize the fixture depending on the shape, size, and material of the fabric parts to be sewn together. Users are required to redesign/reconfigure the fixture to sew different fabric parts. Our robotic sewing system is based on the concept of fixture-free sewing, i.e., the pose of the set of stacked fabric parts is controlled by the end-effectors without using the fixture. The internal force applied to the fabric parts by the end-effectors is used to keep the fabric parts flat, and the position of the fabric parts is controlled by the motions of the end-effectors in coordination with the proposed vision-based seam line tracking control. The proposed robotic sewing system provides practitioners with a new approach to fixture-free automatic sewing of fabric parts.

This paper proposes CNN-based visual servoing for simultaneous positioning and flattening of a soft fabric part placed on a table by a dual manipulator system. We propose a network for multimodal data processing of grayscale images captured by a camera and force/torque applied to force sensors. The training dataset is collected by moving the real manipulators, which enables the network to map the captured images and force/torque to the manipulator's motion in Cartesian space. We apply structured lighting to emphasize the features of the surface of the fabric part since the surface shape of the non-textured fabric part is difficult to recognize by a single grayscale image. Through experiments, we show that the fabric part with unseen wrinkles can be positioned and flattened by the proposed visual servoing scheme.