Robust realtime physics-based motion control for human grasping
Wenping Zhao, Jianjie Zhang, Jianyuan Min, Jinxiang Chai
In ACM Transactions on Graphics, 32(6), November 2013.
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.
Article URL: http://dx.doi.org/10.1145/2508363.2508412
BibTeX format:
@article{Zhao:2013:RRP,
  author = {Wenping Zhao and Jianjie Zhang and Jianyuan Min and Jinxiang Chai},
  title = {Robust realtime physics-based motion control for human grasping},
  journal = {ACM Transactions on Graphics},
  volume = {32},
  number = {6},
  pages = {207:1--207:12},
  month = nov,
  year = {2013},
}
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