Title: Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics,
Abstract: To reduce data collection time for deep learning of robust robotic grasp
plans, we explore training from a synthetic dataset of 6.7 million point
clouds, grasps, and analytic grasp metrics generated from thousands of 3D
models from Dex-Net 1.0 in randomized poses on a table. We use the resulting
dataset, Dex-Net 2.0, to train a Grasp Quality Convolutional Neural Network
(GQ-CNN) model that rapidly predicts the probability of success of grasps from
depth images, where grasps are specified as the planar position, angle, and
depth of a gripper relative to an RGB-D sensor. Experiments with over 1,000
trials on an ABB YuMi comparing grasp planning methods on singulated objects
suggest that a GQ-CNN trained with only synthetic data from Dex-Net 2.0 can be
used to plan grasps in 0.8sec with a success rate of 93% on eight known objects
with adversarial geometry and is 3x faster than registering point clouds to a
precomputed dataset of objects and indexing grasps. The Dex-Net 2.0 grasp
planner also has the highest success rate on a dataset of 10 novel rigid
objects and achieves 99% precision (one false positive out of 69 grasps
classified as robust) on a dataset of 40 novel household objects, some of which
are articulated or deformable. Code, datasets, videos, and supplementary
material are available at this http URL .