Sampling-based Contact-rich Motion Control

ACM Transaction on Graphics (Proceedings of SIGGRAPH 2010)

Libin Liu     KangKang Yin     Michiel van de Panne*     Tianjia Shao     Weiwei Xu
Microsoft Research Asia     UBC*
 

 

Given a motion capture trajectory, we propose a method to reconstruct its open-loop control and the implicit contact forces.

Abstract

Human motions are the product of internal and external forces, but these forces are very difficult to measure in a general setting. Given a motion capture trajectory, we propose a method to reconstruct its open-loop control and the implicit contact forces. The method employs a strategy based on randomized sampling of the control within user-specified bounds, coupled with forward dynamics simulation. Sampling-based techniques are well suited to this task because of their lack of dependence on derivatives, which are difficult to estimate in contact-rich scenarios. They are also easy to parallelize, which we exploit in our implementation on a compute cluster. We demonstrate reconstruction of a diverse set of captured motions, including walking, running, and contact rich tasks such as rolls and kip-up jumps. We further show how the method can be applied to physically based motion transformation and retargeting, physically plausible motion variations, and referencetrajectory- free idling motions. Alongside the successes, we point out a number of limitations and directions for future work.

Paper & Video

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BibTeX
@ARTICLE{2010-TOG-sampControl,
    author = {Libin Liu and KangKang Yin and Michiel van de Panne and Tianjia Shao and Weiwei Xu},
    title = {Sampling-based Contact-rich Motion Control},
    journal = {ACM Transctions on Graphics},
    year = {2010},
    volume = {29},
    number = {4},
    pages = {Article 128}
}