Sampling-based contact-rich motion control

  • Authors:
  • Libin Liu;KangKang Yin;Michiel van de Panne;Tianjia Shao;Weiwei Xu

  • Affiliations:
  • Tsinghua University;Microsoft Research Asia;University of British Columbia;Tsinghua University;Microsoft Research Asia

  • Venue:
  • ACM SIGGRAPH 2010 papers
  • Year:
  • 2010

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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 reference-trajectory-free idling motions. Alongside the successes, we point out a number of limitations and directions for future work.