Transparent object perception is a rapidly developing research problem in artificial
intelligence. The ability to perceive transparent objects enables robots to achieve higher …

Visual affordance recognition is an important research topic in robotics, human-computer
interaction, and other computer vision tasks. In recent years, deep learning-based …

Tubular objects such as test tubes are common in chemistry and life sciences research
laboratories, and robots that can handle them have the potential to accelerate experiments …

Affordance detection is a challenging problem with a wide variety of robotic applications.
Traditional affordance detection methods are limited to a predefined set of affordance labels …

Picking up transparent objects is still a challenging task for robots. The visual properties of
transparent objects such as reflection and refraction make the current grasping methods that …

Transparent objects are encountered frequently in our daily lives, yet recognizing them
poses challenges for conventional vision sensors due to their unique material properties, not …

Most existing methods for category-level pose estimation rely on object point clouds.
However, when considering transparent objects, depth cameras are usually not able to …

Affordance detection presents intricate challenges and has a wide range of robotic
applications. Previous works have faced limitations such as the complexities of 3D object …

The grasping of transparent objects is challenging but of significance to robots. In this article,
a visual–tactile fusion framework for transparent object grasping in complex backgrounds is …

Transparent objects are a common part of daily life, but their unique optical properties make
estimating their 6D pose a challenging task. In this letter, we propose TGF-Net, a monocular …