GraspCNN: Real-Time Grasp Detection Using a New Oriented Diameter Circle Representation

This paper proposes GraspCNN, an approach to grasp detection where a feasible robotic grasp is detected as an oriented diameter circle in RGB image, using a single convolutional neural network. By detecting robotic grasps as oriented diameter circles, grasp representation is thereby simplified. In addition to our novel grasp representation, a grasp pose localization algorithm is proposed to project an oriented diameter circle back to a 6D grasp pose in point cloud. GraspCNN predicts feasible grasping circles and grasp probabilities directly from RGB image. Experiments show that GraspCNN achieves a 96.5% accuracy on the Cornell Grasping Dataset, outperforming existing one-stage detectors for grasp detection. GraspCNN is fast and stable, which can process RGB image at 50 fps and meet the requirements of real-time applications. To detect objects and locate feasible grasps simultaneously, GraspCNN is executed in parallel with YOLO, which achieves outstanding performance on both object detection and grasp detection.

[1]  Sergey Levine,et al.  QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation , 2018, CoRL.

[2]  Dieter Fox,et al.  6-DOF GraspNet: Variational Grasp Generation for Object Manipulation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[3]  Silvio Savarese,et al.  DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Peter Corke,et al.  Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach , 2018, Robotics: Science and Systems.

[5]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[6]  Joseph Redmon,et al.  Real-time grasp detection using convolutional neural networks , 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Hei Law,et al.  CornerNet: Detecting Objects as Paired Keypoints , 2018, International Journal of Computer Vision.

[8]  Yann LeCun,et al.  Synergistic Face Detection and Pose Estimation with Energy-Based Models , 2004, J. Mach. Learn. Res..

[9]  Danica Kragic,et al.  Grasp Moduli Spaces , 2013, Robotics: Science and Systems.

[10]  Dieter Fox,et al.  PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes , 2017, Robotics: Science and Systems.

[11]  Honglak Lee,et al.  Deep learning for detecting robotic grasps , 2013, Int. J. Robotics Res..

[12]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[13]  Dongwon Park,et al.  Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images , 2018, ArXiv.

[14]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  N. Kruger,et al.  Learning object-specific grasp affordance densities , 2009, 2009 IEEE 8th International Conference on Development and Learning.

[17]  Josep M. Porta,et al.  Global Optimization of Robotic Grasps , 2011, Robotics: Science and Systems.

[18]  Jianbin Tang,et al.  GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices , 2018, IJCAI.

[19]  Alexander Herzog,et al.  Learning of grasp selection based on shape-templates , 2014, Auton. Robots.

[20]  Patricio A. Vela,et al.  Real-World Multiobject, Multigrasp Detection , 2018, IEEE Robotics and Automation Letters.

[21]  Quoc V. Le,et al.  Learning to grasp objects with multiple contact points , 2010, 2010 IEEE International Conference on Robotics and Automation.

[22]  Ashutosh Saxena,et al.  Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..

[23]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).