Grasping is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

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 …

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

Grasping a novel target object in constrained environments (eg, walls, bins, and shelves)
requires intensive reasoning about grasp pose reachability to avoid collisions with the …

Contemporary grasp detection approaches employ deep learning to achieve robustness to
sensor and object model uncertainty. The two dominant approaches design either grasp …

Interactive robotic grasping using natural language is one of the most fundamental tasks in
human-robot interaction. However, language can be a source of ambiguity, particularly …

Deep learning has been widely used for inferring robust grasps. Although human-labeled
RGB-D datasets were initially used to learn grasp configurations, preparation of this kind of …

Robotic grasping is one of the most fundamental robotic manipulation tasks and has been
actively studied. However, how to quickly teach a robot to grasp a novel target object in …

Robots in the real world frequently come across identical objects in dense clutter. When
evaluating grasp poses in these scenarios, a target-driven grasping system requires …