Grasp moduli spaces and spherical harmonics

In this work, we present a novel representation which enables a robot to reason about, transfer and optimize grasps on various objects by representing objects and grasps on them jointly in a common space. In our approach, objects are parametrized using smooth differentiable functions which are obtained from point cloud data via a spectral analysis. We show how, starting with point cloud data of various objects, one can utilize this space consisting of grasps and smooth surfaces in order to continuously deform various surface/grasp configurations with the goal of synthesizing force closed grasps on novel objects. We illustrate the resulting shape space for a collection of real world objects using multidimensional scaling and show that our formulation naturally enables us to use gradient ascent approaches to optimize and simultaneously deform a grasp from a known object towards a novel object.

[1]  Gerd Hirzinger,et al.  Grasping the dice by dicing the grasp , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[2]  Tamim Asfour,et al.  Planning grasps for robotic hands using a novel object representation based on the medial axis transform , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[4]  Antonio Bicchi,et al.  Dexterous Grippers: Putting Nonholonomy to Work for Fine Manipulation , 2002, Int. J. Robotics Res..

[5]  Danica Kragic,et al.  Minimum volume bounding box decomposition for shape approximation in robot grasping , 2008, 2008 IEEE International Conference on Robotics and Automation.

[6]  Daniel Sidobre,et al.  Efficient models for grasp planning with a multi-fingered hand , 2012, Robotics Auton. Syst..

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

[8]  Moo K. Chung,et al.  Computational Neuroanatomy: The Methods , 2012 .

[9]  Kai Hormann,et al.  Surface Parameterization: a Tutorial and Survey , 2005, Advances in Multiresolution for Geometric Modelling.

[10]  Moo K. Chung,et al.  Large-Scale Modeling of Parametric Surfaces Using Spherical Harmonics , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[11]  Danica Kragic,et al.  Learning a dictionary of prototypical grasp-predicting parts from grasping experience , 2013, 2013 IEEE International Conference on Robotics and Automation.

[12]  Alla Sheffer,et al.  Fundamentals of spherical parameterization for 3D meshes , 2003, ACM Trans. Graph..

[13]  G. Wahba.  Spline models for observational data , 1990 .

[14]  Vijay Kumar,et al.  Robotic grasping and contact: a review , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[15]  Neil A. Dodgson,et al.  Advances in Multiresolution for Geometric Modelling , 2005 .

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

[17]  Matthias Zwicker,et al.  Meshing Point Clouds Using Spherical Parameterization , 2004, PBG.

[18]  Marc Alexa,et al.  Recent Advances in Mesh Morphing , 2002, Comput. Graph. Forum.

[19]  Moo K. Chung,et al.  Weighted Fourier Series Representation and Its Application to Quantifying the Amount of Gray Matter , 2007, IEEE Transactions on Medical Imaging.

[20]  Máximo A. Roa,et al.  Transferring functional grasps through contact warping and local replanning , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.