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 …

The robotic grasping task persists as a modern industry problem that seeks autonomous,
fast implementation, and efficient techniques. Domestic robots are also a reality demanding …

In order to complete many complex operations and attain more general-purpose utility,
robotic grasp is a necessary skill to master. As the most common essential action of robots in …

This paper concerns the problem of how to learn to grasp dexterously, so as to be able to
then grasp novel objects seen only from a single viewpoint. Recently, progress has been …

Controlling and quantifying the presence of Posidonia Oceanica (PO) in the Mediterranean
sea is crucial for the conservation of these endemic ecosystems and to underscore the …

Machine learning has made breakthroughs in areas such as computer vision and natural
language processing. In recent years, more and more research has been done on the …

We present an algorithm that discovers grasp pose solutions for multiple grasp types for a
multi-fingered mechanical gripper using partially-sensed point clouds of unknown objects …

In this paper, we propose a novel Voting based Grasp Pose Network (VGPN) to detect 6-
DoF grasps in cluttered scenes. The motivation of this paper is that local object geometry …

Accessing software resources via the Cloud has become increasingly popular as a means to
configure and manage automation systems with reduced infrastructure overhead. Dex-Net …

Existing grasp planning networks often learn from labeled images with grasp examples to
eliminate the need for training through physical grasp attempts. As a result, trained networks …