Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection
Haojie Huang, Dian Wang, Xupeng Zhu, Robin Walters, Robert Platt
Abstract
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. Videos and code are available at https://haojhuang.github.io/edge_grasp_page/.
Topics & Concepts
GRASPPoint cloudComputer scienceInvariant (physics)Artificial intelligenceCloud computingEnhanced Data Rates for GSM EvolutionComputer visionGraphSet (abstract data type)Code (set theory)Point (geometry)Object (grammar)Theoretical computer scienceProgramming languageMathematicsGeometryMathematical physicsOperating systemRobot Manipulation and LearningHand Gesture Recognition SystemsManufacturing Process and Optimization