Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection
This addresses the problem of robotic grasping for practical applications, but appears incremental as it builds on prior grasp detection methods.
The paper tackles 6-DoF grasp pose detection from point clouds, proposing a novel graph-based SE(3)-invariant method that achieves better grasp success rates compared to existing literature.
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.