BIG-Net: Deep Learning for Grasping With a Bio-Inspired Soft Gripper

Abstract

In this letter, a grasping neural network for a bio-inspired gripper (BIG-Net) trained on a synthetic dataset is proposed for the picking of novel objects. The grasp feasibility is evaluated by tracking the deformation of the soft gripping pad and three types of gripping forces during simulation. Over 420 K grasp scenes with 4.3 B grasps have been synthesized with stacked objects to train the neural network, instead of isolated objects in many existing methods. The BIG-Net takes in a depth image and provides pixel-wise grasp parameters for a grasp scene. Various experiments in both simulation and real world indicate that the BIG-Net grasping method outperforms the traditional and state-of-the-art methods. It achieves the average grasp success rates of 94% for the random picking of household items in clutter and 86% for adversarial items at real-time speeds (25 ms).