5 accepted papers
Achieving accurate garment grasping under dynamically changing illumination is crucial for all-day operation of service robots. However, the reduced illumination in low-light scenes severely degrades garment structural features, leading to a significant drop in grasping robustness. Existing methods
Robotic cloth grasping is a key component in many robotic cloth manipulation scenarios, such as automated wardrobe management, clothing laundering, and assisted dressing. Due to the deformability and large surface of cloth, which distinguishes it from conventional rigid targets, most current studies
The challenge of multimodal semantic segmentation lies in establishing semantically consistent and segmentable multimodal fusion features under conditions of significant visual feature discrepancies. Existing methods commonly construct cross-modal self-attention fusion frameworks or introduce additi…
Garment grasping in low-light environments is a critical challenge for domestic intelligent robots, yet existing research has not sufficiently addressed this issue. In low-light conditions, the scarcity of visual features due to insufficient illumination causes different categories of garments to ex
Recognizing various types of clothing is crucial for robotic clothing manipulation tasks, such as garment organization and robot-assisted dressing. Unlike rigid object recognition, clothing recognition remains a challenging task due to the diverse forms introduced by flexible deformations. However,