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This paper introduces Touch G.O.G., a novel gripper and perception/control framework that enables a single robotic arm to perform bimanual cloth manipulation. The system uses a vision-based tactile sensor and a Vision Transformer pipeline (PC-Net and PE-Net) to classify cloth parts and estimate edge poses from tactile images. A synthetic data generator (SD-Net) reduces annotation effort by producing realistic tactile images, enabling the system to achieve 96% accuracy in cloth part classification and reliable cloth unfolding in real-world experiments.
Unlock bimanual-level cloth manipulation with a single robotic arm using a novel tactile gripper and vision-based perception framework.
Robotic cloth manipulation remains challenging due to the high-dimensional state space of fabrics, their deformable nature, and frequent occlusions that limit vision-based sensing. Although dual-arm systems can mitigate some of these issues, they increase hardware and control complexity. This paper presents Touch G.O.G., a compact vision-based tactile gripper and perception/control framework for single-arm bimanual cloth manipulation. The proposed framework combines three key components: (1) a novel gripper design and control strategy for in-gripper cloth sliding with a single robot arm, (2) a Vision Foundation Model-backboned Vision Transformer pipeline for cloth part classification (PC-Net) and edge pose estimation (PE-Net) using real and synthetic tactile images, and (3) an encoder-decoder synthetic data generator (SD-Net) that reduces manual annotation by producing high-fidelity tactile images. Experiments show 96% accuracy in distinguishing edges, corners, interior regions, and grasp failures, together with sub-millimeter edge localization and 4.5{\deg} orientation error. Real-world results demonstrate reliable cloth unfolding, even for crumpled fabrics, using only a single robotic arm. These results highlight Touch G.O.G. as a compact and cost-effective solution for deformable object manipulation.
