Using human tools can significantly benefit robots in many application domains. Such ability
would allow robots to solve problems that they were unable to without tools. However, robot …

Gras** unknown objects in unstructured environments is one of the most challenging and
demanding tasks for robotic bin picking systems. Develo** a holistic approach is crucial to …

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 …

In this work, we present a multi-stage pipeline that aims to accurately predict suction grasps
for objects with varying properties in cluttered and complex scenes. Existing methods face …

Wire harnesses are essential connecting components in manufacturing industry but are
challenging to be automated in industrial tasks such as bin picking. They are long, flexible …

In today's highly competitive industrial environment, digital transformation and smart
manufacturing have become crucial strategies for enhancing competitiveness. Companies …

This paper proposes a novel bin picking framework, two-stage gras**, aiming at precise
gras** of cluttered small objects. Object density estimation and rough gras** are …

In this article, we consider a certifiable object pose estimation problem, where—given a
partial point cloud of an object—the goal is to not only estimate the object pose, but also …

Recent problems in robotics can sometimes only be tackled using machine learning
technologies, particularly those that utilize deep learning (DL) with transfer learning …

This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a
domain adaptation problem. In this case, we present a global-to-local method to address …