This paper presents a comprehensive survey on vision-based robotic grasping. We
conclude three key tasks during vision-based robotic grasping, which are object localization …

This paper presents a comprehensive survey of data-driven robotic visual grasping
detection (DRVGD) for unknown objects. We review both object-oriented and scene …

The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of
robots that allows the implementation of performing human-like behaviors. Deploying the …

Robotic grasping has the challenge of accurately extracting the graspable target from a
complicated scenario. To address the issue, we propose a 3D vision prediction framework …

Robotic grasping is a fundamental ability for a robot to interact with the environment. Current
methods focus on how to obtain a stable and reliable grasping pose in object level, while …

In this paper, we present an end-to-end approach method using deep learning for grasp
detection. Our method is a real-time processing method for discrete depth image sampling …

To learn grasp constraints from human demonstrations, we propose a method that combines
data-driven grasp constraint learning and one-shot human demonstration of tasks. By …

With the wide application of robots in life and production, robotic grasping is also
experiencing continuous development. However, in practical application, some external …

The task of fully automated picking of novel bin objects that are placed in a densely cluttered
pile poses a significant challenge. It becomes even more challenging if the objects are of …