Data Driven Synthesis of Hand Grasps from 3-D Object Models

Matthias B. Hullin, Reinhard Klein, and Angela Yao (Editors)
Soumajit Majumder, Chen Haojiong, and Angela Yao
In proceedings of Vision, Modeling & Visualization, The Eurographics Association, 2017
 

Abstract

Modeling and predicting human hand grasping interactions is an active area of research in robotics, computer vision and computer graphics. We tackle the problem of predicting plausible hand grasps and the contact points given an input 3-D object model. Such a prediction task can be difficult due to the variations in the 3-D structure of daily use objects as well as the different ways that similar objects can be manipulated. In this work, we formulate grasp synthesis as a constrained optimization problem which takes into account the anthropomorphic and kinematic limitations of a human hand as well as the local and global geometric properties of the interacting object. We evaluate our proposed algorithm on twelve 3-D object models of daily use and demonstrate that our algorithm can successfully predict plausible hand grasps and contact points on the object.

Bibtex

@INPROCEEDINGS{majumder-2017-,
     author = {Majumder, Soumajit and Haojiong, Chen and Yao, Angela},
     editor = {Hullin, Matthias B. and Klein, Reinhard and Yao, Angela},
      title = {Data Driven Synthesis of Hand Grasps from 3-D Object Models},
  booktitle = {Vision, Modeling {\&} Visualization},
       year = {2017},
  publisher = {The Eurographics Association},
   abstract = {Modeling and predicting human hand grasping interactions is an active area of research in robotics,
               computer vision and computer graphics. We tackle the problem of predicting plausible hand grasps and
               the contact points given an input 3-D object model. Such a prediction task can be difficult due to
               the variations in the 3-D structure of daily use objects as well as the different ways that similar
               objects can be manipulated. In this work, we formulate grasp synthesis as a constrained optimization
               problem which takes into account the anthropomorphic and kinematic limitations of a human hand as
               well as the local and global geometric properties of the interacting object. We evaluate our
               proposed algorithm on twelve 3-D object models of daily use and demonstrate that our algorithm can
               successfully predict plausible hand grasps and contact points on the object.},
       isbn = {978-3-03868-049-9},
        doi = {10.2312/vmv.20171258}
}