Recent results suggest that it is possible to grasp a variety of singulated objects with high precision using Convolutional Neural Networks (CNNs)trained on synthetic data. Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences Jeffrey Mahler, Ken Goldberg CoRL 2017 The Dex-Net 4.0 policy consistently clears bins of up to 25 novel objects with reliability greater than 95% at a rate of Grasps, and rewards generated from heaps of three-dimensional objects. Train policies for a parallel-jaw and a vacuum-based suction cup gripper on 5 million synthetic depth images,
(Dex-Net) 4.0, a substantial extension to previous versions of Dex-Net that learns policies for a given set of grippersīy training on synthetic datasets using domain randomization with analytic models of physics and geometry. “ambidextrous” robot grasping, where two or more heterogeneous grippers are used. Optimizing the rate, reliability,Īnd range of UP is difficult due to inherent uncertainty in sensing, control, and contact physics. Universal picking (UP), or reliable robot grasping of a diverse range of novel objects from heaps, is a grand challengeįor e-commerce order fulfillment, manufacturing, inspection, and home service robots. Learning Ambidextrous Robot Grasping Policies Jeffrey Mahler, Matthew Matl, Vishal Satish, Michael Danielczuk, Bill DeRose, Stephen McKinley, Ken Goldberg Science Robotics Dex-Net Database Python API ( Documentation).
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GQ-CNN Python Training Code ( Documentation). HDF5 Database of 3D Objects, Parallel-Jaw Grasps for YuMi, and Robustness Metrics. Ken Goldberg and is currently maintained by the Berkeley AUTOLAB. The project was created by Jeff Mahler and Prof. GQ-CNNs may be useful for quickly planning grasps that can lift and transport a wide variety of objects a physical robot.ĭex-Net 2.1 adds dynamic simulation with pybullet and extends the robust grasping model to the sequential task of bin picking.ĭex-Net 3.0 adds support for suction-based end-effectors.ĭex-Net 4.0 unifies the reward metric across multiple grippers to efficiently train "ambidextrous" grasping policies that can decide which gripper is best for a particular object.ĭex-Net 1.0 was designed for distributed robust grasp analysis in the Cloud across datasets of over 10,000 3D mesh models. The broader goal of the Dex-Net project is to develop highly reliable robot grasping across a wide variety of rigid objects such as tools, household items, packaged goods, and industrial parts.ĭex-Net 2.0 is designed to generated training datasets to learn Grasp Quality Convolutional Neural Networks (GQ-CNN) models that predict the probability of success of candidate parallel-jaw grasps on objects from point clouds. The Dexterity Network (Dex-Net) is a research project including code, datasets, and algorithms for generating datasets of synthetic point clouds, robot parallel-jaw grasps and metrics of grasp robustness based on physics for thousands of 3D object models to train machine learning-based methods to plan robot grasps.
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