Robust realtime physics-based motion control for human grasping
Robust realtime physics-based motion control for human grasping
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DOI：
10.1145/2508363.2508412
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Wenping Zhao;Jianjie Zhang;Jianyuan Min;Jinxiang Chai
中科院分区：
文献类型：
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作者：
Wenping Zhao;Jianjie Zhang;Jianyuan Min;Jinxiang Chai
提出了一种鲁棒的基于物理的人体抓取实时合成运动控制系统。给定要抓取的对象，我们的系统自动计算基于物理的运动控制，从而推进模拟以实现对对象的真实操作。我们的解决方案利用预先录制的运动数据和基于物理的人类抓取模拟。我们首先介绍了一种数据驱动的合成算法，该算法利用大量预先录制的运动数据来生成人类抓取的真实运动。接下来，我们提出了一种基于物理的在线运动控制算法，将合成的运动学运动转化为物理真实的运动。此外，我们为人类抓取开发了一个性能界面，允许用户在单个Kinect摄像头前执行所需的抓取动作。我们通过生成基于物理的运动控制来展示我们方法的力量，用于抓取具有不同属性（如形状，重量，空间方向和摩擦）的物体。我们证明了基于物理的人类抓取运动控制对外部扰动和物理量变化具有鲁棒性。
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.