Gras** 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 overview presents computational algorithms for generating 3D object grasps with
autonomous multi-fingered robotic hands. Robotic gras** has been an active research …

The robotic grasp detection is a great challenge in the area of robotics. Previous work mainly
employs the visual approaches to solve this problem. In this paper, a hybrid deep …

This article develops model-based grasp planning algorithms. It focuses on industrial end-
effectors like grippers and suction cups, and plans grasp configurations considering …

Affordance detection and pose estimation are of great importance in many robotic
applications. Their combination helps the robot gain an enhanced manipulation capability …

Gras** has always been a great challenge for robots due to its lack of the ability to well
understand the perceived sensing data. In this work, we propose an end-to-end deep vision …

Neuro-psychological findings have shown that human perception of objects is based on part
decomposition. Most objects are made of multiple parts which are likely to be the entities …

This work presents an approach for part-based grasp planning in point clouds. A complete
pipeline is proposed that allows a robot manipulator equipped with a range camera to …

This paper proposes a method for bin-picking for objects without assuming the precise
geometrical model of objects. We consider the case where the shape of objects are not …