Human arm movements are highly stereotypical under a large variety of experimental conditions. This is striking due to the high redundancy of the human musculoskeletal system, which in principle allows many possible trajectories toward a goal. Many researchers hypothesize that through evolution, learning, and adaption, the human system has developed optimal control strategies to select between these possibilities. Various optimality principles were proposed in the literature that reproduce human-like trajectories in certain conditions. However, these studies often focus on a single cost function and use simple torque-driven models of motion generation, which are not consistent with human muscle-actuated motion. The underlying structure of our human system, with the use of muscle dynamics in interaction with the control principles, might have a significant influence on what optimality principles best model human motion. To investigate this hypothesis, we consider a point-to-manifold reaching task that leaves the target underdetermined. Given hypothesized motion objectives, the control input is generated using Bayesian optimization, which is a machine learning based method that trades-off exploitation and exploration. Using numerical simulations with Hill-type muscles, we show that a combination of optimality principles best predicts human point-to-manifold reaching when accounting for the muscle dynamics.

中文翻译：

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人体点对流形达到肌肉动力学的最优原则

在各种各样的实验条件下，人类手臂的运动是高度刻板的。这是惊人的，因为人类肌肉骨骼系统的高度冗余，原则上允许许多可能的轨迹实现目标。许多研究人员假设，通过进化、学习和适应，人类系统已经开发出最佳控制策略来在这些可能性之间进行选择。文献中提出了各种优化原则，可以在某些条件下重现类人轨迹。然而，这些研究通常侧重于单个成本函数，并使用简单的扭矩驱动运动生成模型，这与人类肌肉驱动的运动不一致。我们人类系统的基本结构，使用肌肉动力学与控制原理相互作用，可能对哪种最优性原则最好地模拟人体运动产生重大影响。为了研究这一假设，我们考虑了一个点到流形到达任务，该任务使目标不确定。给定假设的运动目标，使用贝叶斯优化生成控制输入，这是一种基于机器学习的方法，可以在开发和探索之间进行权衡。使用 Hill 型肌肉的数值模拟，我们表明在考虑肌肉动力学时，最优原则的组合最能预测人类点到歧管的到达。控制输入​​是使用贝叶斯优化生成的，贝叶斯优化是一种基于机器学习的方法，可以在开发和探索之间进行权衡。使用 Hill 型肌肉的数值模拟，我们表明在考虑肌肉动力学时，最优原则的组合最能预测人类点到歧管的到达。控制输入​​是使用贝叶斯优化生成的，贝叶斯优化是一种基于机器学习的方法，可以在开发和探索之间进行权衡。使用 Hill 型肌肉的数值模拟，我们表明在考虑肌肉动力学时，最优原则的组合最能预测人类点到歧管的到达。