We present a novel approach to perform object-independent grasp synthesis from depth
images via deep neural networks. Our generative grasping convolutional neural network …

This paper presents a real-time, object-independent grasp synthesis method which can be
used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural …

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 …

We review the work on data-driven grasp synthesis and the methodologies for sampling and
ranking candidate grasps. We divide the approaches into three groups based on whether …

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 …

Society is becoming more automated with robots beginning to perform most tasks in
factories and starting to help out in home and office environments. One of the most important …

Deep learning-based robotic grasping has made significant progress thanks to algorithmic
improvements and increased data availability. However, state-of-the-art models are often …

Collecting grasp data for learning and benchmarking purposes is very expensive. It would
be helpful to have a standard database of graspable objects, along with a set of stable …

Machine vision-based planar grasping detection is challenging due to uncertainty about
object shape, pose, size, etc. Previous methods mostly focus on predicting discrete gripper …

We present a system for robust robot skill acquisition from kinesthetic demonstrations. This
system allows a robot to learn a simple goal-directed gesture and correctly reproduce it …