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An Optimal Feedback Control Framework for Grasping Objects with Position Uncertainty

机译:具有位置不确定性的对象的最优反馈控制框架

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摘要

As we move, the relative location between our hands and objects changes in uncertain ways due to noisy motor commands and imprecise and ambiguous sensory information. The impressive capabilities humans display for interacting and manipulating objects with position uncertainty suggest that our brain maintains representations of location uncertainty and builds compensation for uncertainty into its motor control strategies. Our previous work demonstrated that specific control strategies are used to compensate for location uncertainty. However, it is an open question whether compensation for position uncertainty in grasping is consistent with the stochastic optimal feedback control, mainly due to the difficulty of modeling natural tasks within this framework. In this study, we develop a stochastic optimal feedback control model to evaluate the opti-mality of human grasping strategies. We investigate the properties of the model through a series of simulation experiments and show that it explains key aspects of previously observed compensation strategies. It also provides a basis for individual differences in terms of differential control costs—the controller compensates only to the extent that performance benefits in terms of making stable grasps outweigh the additional control costs of compensation. These results suggest that stochastic optimal feedback control can be used to understand uncertainty compensation in complex natural tasks like grasping.
机译:当我们移动时,由于嘈杂的运动指令以及不精确和含糊的感觉信息,我们的手和物体之间的相对位置以不确定的方式变化。人类在与位置不确定性进行交互和操纵时所显示的令人印象深刻的功能表明,我们的大脑可以保持位置不确定性的表示,并将不确定性的补偿构建到其运动控制策略中。我们以前的工作表明,使用了特定的控制策略来补偿位置不确定性。然而,这主要是由于难以在此框架内对自然任务进行建模,因此对抓握中位置不确定性的补偿是否与随机最优反馈控制相一致是一个悬而未决的问题。在这项研究中,我们建立了随机的最优反馈控制模型,以评估人类掌握策略的最优性。我们通过一系列仿真实验研究了模型的属性,并表明它解释了先前观察到的补偿策略的关键方面。它还为差异控制成本方面的个体差异提供了依据-控制器仅在进行稳定控制方面的性能收益超过补偿的其他控制成本的范围内进行补偿。这些结果表明,随机最优反馈控制可用于理解复杂自然任务(如抓紧力)中的不确定性补偿。

著录项

  • 来源
    《Neural computation》 |2011年第10期|p.2511-2536|共26页
  • 作者单位

    Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, U.S.A;

    Department of Computer Science and Engineering and Department of Psychology, University of Minnesota, Minneapolis, MN 55455, U.S.A;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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