FFHNet: Generating Multi-Fingered Robotic Grasps for Unknown Objects in Real-time
Vincent Mayer, Qian Feng, Jun Deng, Yunlei Shi, Zhaopeng Chen, Alois Knoll
Abstract
Grasping unknown objects with multi-fingered hands at high success rates and in real-time is an unsolved problem. Existing methods are limited in the speed of grasp synthesis or the ability to synthesize a variety of grasps from the same observation. We introduce Five-finger Hand Net (FFHNet), an ML model which can generate a wide variety of high-quality multi-fingered grasps for unseen objects from a single view. Generating and evaluating grasps with FFHNet takes only 30ms on a commodity GPU. To the best of our knowledge, FFHNet is the first ML-based real-time system for multi-fingered grasping with the ability to perform grasp inference at 30 frames per second (FPS). For training, we synthetically generate 180k grasp samples for 129 objects. We are able to achieve 91% grasping success for unknown objects in simulation and we demonstrate the model's capabilities of synthesizing high-quality grasps also for real unseen objects.