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 …

To reduce data collection time for deep learning of robust robotic grasp plans, we explore
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp …

We approach the problem of high-DOF reaching-and-grasping via learning joint planning of
grasp and motion with deep reinforcement learning. To resolve the sample efficiency issue …

The empirical success of reinforcement learning (RL) in contact-rich manipulation leaves
much to be understood from a model-based perspective, where the key difficulties are often …

Grasping is among the most fundamental and long-lasting problems in robotics study. This
paper studies the problem of 6-DoF (degree of freedom) grasping by a parallel gripper in a …

We propose to perform imitation learning for dexterous manipulation with multi-finger robot
hand from human demonstrations, and transfer the policy to the real robot hand. We …

A robotic grasping simulator, called Graspit!, is presented as versatile tool for the grasping
community. The focus of the grasp analysis has been on force-closure grasps, which are …

This paper addresses the problem of computing stable grasps of 2-D polygonal objects. We
consider the case of a hand equipped with three hard fingers and assume point contact with …

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