The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining
a good grasp requires algorithms to automatically determine proper contact points on the …

This paper presents a comprehensive review of ground agricultural robotic systems and
applications with special focus on harvesting that span research and commercial products …

This paper presents the Dexterity Network (Dex-Net) 1.0, a dataset of 3D object models and
a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust …

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

This paper presents a method for one-shot learning of dexterous grasps and grasp
generation for novel objects. A model of each grasp type is learned from a single kinesthetic …

Designing robust end-effector plays a crucial role in performance of a robot workcell. Design
automation of industrial grippers' fingers/jaws is therefore of the highest interest in the robot …

This paper presents a novel robot grasping planning approach that extracts grasp strategies
(grasp type, and thumb placement and direction) from human demonstration and integrates …

An important challenge in robotics is to achieve robust performance in object grasping and
manipulation, dealing with noise and uncertainty. This paper presents an approach for …

Robot grasping is often formulated as a learning problem. With the increasing speed and
quality of physics simulations, generating large-scale grasping data sets that feed learning …

To perform robust grasping, a multi-fingered robotic hand should be able to adapt its
grasping configuration, ie, how the object is grasped, to maintain the stability of the grasp …