Robust realtime physics-based motion control for human grasping

  • Authors:
  • Wenping Zhao;Jianjie Zhang;Jianyuan Min;Jinxiang Chai

  • Affiliations:
  • Texas A&M University and University of Science and Technology of China;Texas A&M University;Texas A&M University;Texas A&M University

  • Venue:
  • ACM Transactions on Graphics (TOG)
  • Year:
  • 2013

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Abstract

This paper presents a robust physics-based motion control system for realtime synthesis of human grasping. Given an object to be grasped, our system automatically computes physics-based motion control that advances the simulation to achieve realistic manipulation with the object. Our solution leverages prerecorded motion data and physics-based simulation for human grasping. We first introduce a data-driven synthesis algorithm that utilizes large sets of prerecorded motion data to generate realistic motions for human grasping. Next, we present an online physics-based motion control algorithm to transform the synthesized kinematic motion into a physically realistic one. In addition, we develop a performance interface for human grasping that allows the user to act out the desired grasping motion in front of a single Kinect camera. We demonstrate the power of our approach by generating physics-based motion control for grasping objects with different properties such as shapes, weights, spatial orientations, and frictions. We show our physics-based motion control for human grasping is robust to external perturbations and changes in physical quantities.