GKNet: Grasp keypoint network for grasp candidates detection

Fully Convolutional Grasp Detection Network with Oriented Anchor Box

In this paper, we present a real-time approach to predict multiple grasping poses for a parallel-plate robotic gripper using RGB images. A model with oriented anchor box mechanism is proposed and a new matching strategy is used during the training process. An end-to-end fully convolutional neural network is employed in our work. The network consists of two parts: the feature extractor and multi...

Classification based Grasp Detection using Spatial Transformer Network

Robotic grasp detection task is still challenging, particularly for novel objects. With the recent advance of deep learning, there have been several works on detecting robotic grasp using neural networks. Typically, regression based grasp detection methods have outperformed classification based detection methods in computation complexity with excellent accuracy. However, classification based ro...

Grasp.

Grasp Pose Detection in Point Clouds

Recently, a number of grasp detection methods have been proposed that can be used to localize robotic grasp configurations directly from sensor data without estimating object pose. The underlying idea is to treat grasp perception analogously to object detection in computer vision. These methods take as input a noisy and partially occluded RGBD image or point cloud and produce as output pose est...

Grasp Invariance

This paper introduces a principle to guide the design of finger form: invariance of contact geometry over some continuum of varying shape and/or pose of the grasped object in the plane. Specific applications of this principle include scaleinvariant and pose-invariant grasps. Under specific conditions, the principle gives rise to spiral shaped fingers, including logarithmic spirals and straight ...