Robust realtime physics-based motion control for human grasping

Wenping Zhao, Jianjie Zhang, Jianyuan Min, Jinxiang Chai
2013 ACM Transactions on Graphics  
Figure 1 : Realtime generation of physics-based motion control for human grasping: (left) automatic grasping of objects with different shapes, weights, frictions, and spatial orientations; (right) performance interfaces: acting out the desired grasping motion in front of a single Kinect. 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
more » ... hat 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.
doi:10.1145/2508363.2508412 fatcat:2kvpvg4qjjaoddibytczenlh6m