Sampling-based contact-rich motion control

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Sampling-based contact-rich motion control
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...
Libin Liu, KangKang Yin, Michiel van de Panne, Tia
Added 28 Jul 2010
Updated 29 Jul 2010
Type Conference
Year 2010
Authors Libin Liu, KangKang Yin, Michiel van de Panne, Tianjia Shao, Weiwei Xu
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