Adaptive Fuzzy Gaussian Mixture Models for Shape Approximation in Robot Grasping

AbstractRobotic grasping has always been a challenging task for both service and industrial robots. The ability of grasp planning for novel objects is necessary for a robot to autonomously perform grasps under unknown environments. In this work, we consider the task of grasp planning for a parallel gripper to grasp a novel object, given an RGB image and its corresponding depth image taken from a single view. In this paper, we show that this problem can be simplified by modeling a novel object as a set of simple shape primitives, such as ellipses. We adopt fuzzy Gaussian mixture models (GMMs) for novel objects’ shape approximation. With the obtained GMM, we decompose the object into several ellipses, while each ellipse is corresponding to a grasping rectangle. After comparing the grasp quality among these rectangles, we will obtain the most proper part for a gripper to grasp. Extensive experiments on a real robotic platform demonstrate that our algorithm assists the robot to grasp a variety of novel objects with good grasp quality and computational efficiency.

[1]  Henrik I. Christensen,et al.  Automatic grasp planning using shape primitives , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[2]  Stefan Leutenegger,et al.  Deep learning a grasp function for grasping under gripper pose uncertainty , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Siddhartha S. Srinivasa,et al.  Physics-Based Grasp Planning Through Clutter , 2012, Robotics: Science and Systems.

[4]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[5]  Van-Due Nguyen,et al.  Constructing stable force-closure grasps , 1986 .

[6]  H. Hanafusa,et al.  Stable Prehension by a Robot Hand with Elastic Fingers , 1977 .

[7]  Ashutosh Saxena,et al.  Efficient grasping from RGBD images: Learning using a new rectangle representation , 2011, 2011 IEEE International Conference on Robotics and Automation.

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Christopher Kanan,et al.  Robotic grasp detection using deep convolutional neural networks , 2016, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[11]  Peter K. Allen,et al.  Pose error robust grasping from contact wrench space metrics , 2012, 2012 IEEE International Conference on Robotics and Automation.

[12]  David J. Montana,et al.  The condition for contact grasp stability , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[13]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[14]  Peter K. Allen,et al.  Graspit! A versatile simulator for robotic grasping , 2004, IEEE Robotics & Automation Magazine.

[15]  Dieter Fox,et al.  A large-scale hierarchical multi-view RGB-D object dataset , 2011, 2011 IEEE International Conference on Robotics and Automation.

[16]  Jean Ponce,et al.  On Computing Two-Finger Force-Closure Grasps of Curved 2D Objects , 1993, Int. J. Robotics Res..

[17]  W. Peizhuang.  Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek) , 1983 .

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

[19]  Matei T. Ciocarlie,et al.  The Columbia grasp database , 2009, 2009 IEEE International Conference on Robotics and Automation.

[20]  C. L. Philip Chen,et al.  I-Ching Divination Evolutionary Algorithm and its Convergence Analysis , 2017, IEEE Transactions on Cybernetics.

[21]  Mengyin Fu,et al.  Robot manipulator self-identification for surrounding obstacle detection , 2016, Multimedia Tools and Applications.

[22]  John F. Canny,et al.  Planning optimal grasps , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

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

[24]  Peter K. Allen,et al.  An SVM learning approach to robotic grasping , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[25]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[26]  Honghai Liu,et al.  Fuzzy Gaussian Mixture Models , 2012, Pattern Recognit..

[27]  Tong Zhang,et al.  Design of Highly Nonlinear Substitution Boxes Based on I-Ching Operators , 2018, IEEE Transactions on Cybernetics.

[28]  B. Dizioglu,et al.  Mechanics of form closure , 1984 .

[29]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Quoc V. Le,et al.  Grasping novel objects with depth segmentation , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[31]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[32]  Martin A. Riedmiller,et al.  A learned feature descriptor for object recognition in RGB-D data , 2012, 2012 IEEE International Conference on Robotics and Automation.

[33]  J. K. Salisbury,et al.  Kinematic and Force Analysis of Articulated Mechanical Hands , 1983 .

[34]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .