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 …

This research presents a soft gripper for apple harvesting to provide constant-pressure
clamping and avoid fruit damage during slippage, to reduce the potential danger of damage …

The Advancements in tactile sensors and machine learning techniques open new
opportunities for achieving intelligent grasping in robotics. Traditional robot is limited in its …

Soft pneumatic bending actuators are commonly used in robotic grasping applications and
can be applied for handling both delicate and irregularlyshaped objects. Because these soft …

Soft gripper design is a rising area of research due of its great possibilities in automation.
One difficult problem in robot design is the ability to grasp a broader variety of items with …

Anthropomorphic robotic hands are seeking to achieve key features such as multi-degree-of-
freedom motion ability, bi-directional actuation, high adaptability, and sufficient stiffness. In …

The development of robotic hand that can imitate human movements has always been an
important research topic. In this paper, a linkage-driven underactuated three-finger hand is …

Grasping objects poses a significant challenge for autonomous robotic manipulation in
unstructured and cluttered environments. Despite recent advancements in 6-DoF (degree of …

Incremental few-shot learning is highly expected for practical robotics applications. On one
hand, robot is desired to learn new tasks quickly and flexibly using only few annotated …

Existing industrial and household robots suffer from insufficient adaptability under
increasingly complex scenes and challenges in operating objects. This prevents the …