Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic grasping and manipulation. Current trends and …

Object pose estimation is a core perception task that enables, for example, object
manipulation and scene understanding. The widely available, inexpensive, and high …

Abstract 3D object pose estimation for robotic grasping and manipulation is a crucial task in
the manufacturing industry. In cluttered and occluded scenes, the 6D pose estimation of the …

We present a generic and robust sim-to-real deep-learning-based framework, namely S2R-
Pick, for fast and accurate object recognition and localization in industrial robotic bin picking …

Robot manipulation, for example, pick-and-place manipulation, is broadly used for intelligent
manufacturing with industrial robots, ocean engineering with underwater robots, service …

Suction is an important solution for the long-standing robotic grasping problem. Compared
with other kinds of grasping, suction grasping is easier to represent and often more reliable …

Industrial robots have been utilized for factory automation due to their high repeatability.
Along with the development of visual servo and machine learning techniques, various vision …

Recently, 6DoF object pose estimation has become increasingly important for a broad range
of applications in the fields of virtual reality, augmented reality, autonomous driving, and …

In this paper, we introduce a novel single shot approach for 6D object pose estimation of
rigid objects based on depth images. For this purpose, a fully convolutional neural network …

In robotic bin-picking applications, the perception of texture-less, highly reflective parts is a
valuable but challenging task. The high glossiness can introduce fake edges in RGB images …