Sampling-based contact-rich motion control
Sampling-based contact-rich motion control
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DOI：
10.1145/1833349.1778865
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
Libin Liu;KangKang Yin;M. V. D. Panne;Tianjia Shao;Weiwei Xu
中科院分区：
文献类型：
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作者：
Libin Liu;KangKang Yin;M. V. D. Panne;Tianjia Shao;Weiwei Xu
人体运动是内力和外力的产物，但这些力在一般情况下很难测量。在给定运动捕捉轨迹的情况下，我们提出了一种重构其开环控制和隐式接触力的方法。该方法采用了在用户指定的范围内对控制进行随机采样的策略，并结合前向动态仿真。基于抽样的技术非常适合这项任务，因为它们不依赖于衍生品，而衍生品在接触丰富的情况下很难估计。它们也很容易并行化，我们在计算集群上的实现中利用了这一点。我们演示了一组不同的捕获运动的重建，包括行走、跑步和丰富的接触任务，如滚动和跳跃。我们进一步展示了如何将该方法应用于基于物理的运动变换和重定目标、物理上可信的运动变化以及无参考轨迹的空转运动。在取得成功的同时，我们指出了一些局限性和未来工作的方向。
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.