Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection

10/31/2022
by   Haojie Huang, et al.
0

Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped. This important problem has many practical applications. Here we propose a novel method and neural network model that enables better grasp success rates relative to what is available in the literature. The method takes standard point cloud data as input and works well with single-view point clouds observed from arbitrary viewing directions.

READ FULL TEXT

page 6

page 9

research
02/28/2020

REGNet: REgion-based Grasp Network for Single-shot Grasp Detection in Point Clouds

Learning a robust representation of robotic grasping from point clouds i...
research
08/07/2019

Grasp Type Estimation for Myoelectric Prostheses using Point Cloud Feature Learning

Prosthetic hands can help people with limb difference to return to their...
research
09/19/2022

Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input

Great success has been achieved in the 6-DoF grasp learning from the poi...
research
04/03/2022

Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3)

Grasp detection of novel objects in unstructured environments is a key c...
research
04/15/2019

Optimization Model for Planning Precision Grasps with Multi-Fingered Hands

Precision grasps with multi-fingered hands are important for precise pla...
research
06/11/2022

E^2PN: Efficient SE(3)-Equivariant Point Network

This paper proposes a new point-cloud convolution structure that learns ...
research
04/05/2022

SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space

We propose the first SE(3)-equivariant coordinate-based network for lear...

Please sign up or login with your details

Forgot password? Click here to reset