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

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 …

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 …

Abstract 6D object pose estimation is important for robotic bin-picking, and serves as a
prerequisite for many downstream industrial applications. However, it is burdensome to …

Most supervised learning-based pose estimation methods for stacked scenes are trained on
massive synthetic datasets. In most cases, the challenge is that the learned network on the …

Current state-of-the-art pose estimation methods are almost launched on segmented RGB-D
images. However, these methods may not apply to more general industrial parts due to a …

We propose a novel keypoint voting 6DoF object pose estimation method, which takes pure
unordered point cloud geometry as input without RGB information. The proposed cascaded …

Object pose estimation is a fundamental computer vision problem with broad applications in
augmented reality and robotics. Over the past decade, deep learning models, due to their …

In this paper, we introduce a novel learning-based approach for grasping known rigid
objects in highly cluttered scenes and precisely placing them based on depth images. Our …

Most industrial parts are parametric and their special properties are not fully explored yet.
This paper proposes a new 6DoF pose estimation network for parametric shapes in stacked …