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

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.