Traditional frame-based cameras, despite their effectiveness and usage in computer vision,
exhibit limitations such as high latency, low dynamic range, high power consumption, and …

This review paper provides a comprehensive analysis of over 100 research papers focused
on the challenges of robotic gras** and the effectiveness of various machine learning …

Robots have always found it challenging to grasp in cluttered scenes because of the
complex background information and changing operating environment. Therefore, in order …

Countersink inspection is crucial in various automated assembly lines, especially in the
aerospace and automotive sectors. Advancements in machine vision introduced automated …

Neuromorphic vision is a bio-inspired technology that has triggered a paradigm shift in the
computer vision community and is serving as a key enabler for a wide range of applications …

Object classification based on tactile perception plays an essential role in robot
manipulation process, as it serves for decision-making for the the downstream manipulation …

Cobots play an essential role in the fourth industrial revolution and the automation of
complex manufacturing processes. However, cobots still face challenges in achieving high …

Event-based cameras are commonly leveraged to mitigate issues such as motion blur, low
dynamic range, and limited time sampling, which plague conventional cameras. However, a …

Gras** is fundamental in various robotic applications, particularly within industrial
contexts. Accurate inference of object properties is a crucial step toward enhancing gras** …

Taking advantage of an event-based camera, the issues of motion blur, low dynamic range
and low time sampling of standard cameras can all be addressed. However, there is a lack …