As personalization technology increasingly orchestrates individualized shopping or
marketing experiences in industries such as logistics, fast-moving consumer goods, and …

We aim to tackle the challenging Few-Shot Object Detection (FSOD) where data-scarce
categories are presented during the model learning. The failure modes of FSOD are …

In this letter, a novel grasp detection neural network Squeeze-and-Excitation ResUNet (SE-
ResUNet) is developed, where the residual block with the channel attention is integrated …

The complex and changeable robotic operating environment will often cause the low
success rate or failure of the robot grasping. This article proposes a grasp pose detection …

Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the
recent progress of consumer-grade RGB-D sensors enables delivering more …

Real-time and accuracy are important evaluation metrics of robotic grasp detection
algorithms. To further improve the accuracy on the premise of ensuring real-time …

Universal grasping for a diverse range of objects is a challenging problem in robotics,
especially in the presence of mixed properties with fragile/rigid and heavy/light. Toward …

In this letter, we propose a novel Grasp Pose Domain Adaptation Network (GPDAN) to
achieve sim-to-real domain adaptation for 6-DoF grasp pose detection. The main task of …

Prediction of Remaining Useful Lifetime (RUL) in the modern manufacturing and automation
workplace for machines and tools is essential in Industry 4.0. This is clearly evident as …

Efficient grasp pose detection is essential for robotic manipulation in cluttered scenes.
However, most methods only utilize point clouds or images for prediction, ignoring the …