Department of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ 86011, U.S.A.
Department of Biology and Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, U.S.A.
Neural Comput. 2023 Apr 18;35(5):853-895. doi: 10.1162/neco_a_01576.
Humans are adept at a wide variety of motor skills, including the handling of complex objects and using tools. Advances to understand the control of voluntary goal-directed movements have focused on simple behaviors such as reaching, uncoupled to any additional object dynamics. Under these simplified conditions, basic elements of motor control, such as the roles of body mechanics, objective functions, and sensory feedback, have been characterized. However, these elements have mostly been examined in isolation, and the interactions between these elements have received less attention. This study examined a task with internal dynamics, inspired by the daily skill of transporting a cup of coffee, with additional expected or unexpected perturbations to probe the structure of the controller. Using optimal feedback control (OFC) as the basis, it proved necessary to endow the model of the body with mechanical impedance to generate the kinematic features observed in the human experimental data. The addition of mechanical impedance revealed that simulated movements were no longer sensitively dependent on the objective function, a highly debated cornerstone of optimal control. Further, feedforward replay of the control inputs was similarly successful in coping with perturbations as when feedback, or sensory information, was included. These findings suggest that when the control model incorporates a representation of the mechanical properties of the limb, that is, embodies its dynamics, the specific objective function and sensory feedback become less critical, and complex interactions with dynamic objects can be successfully managed.
人类擅长各种运动技能,包括处理复杂物体和使用工具。为了深入理解对自愿目标导向运动的控制,研究主要集中在简单的行为上,例如伸手,而不考虑任何额外的物体动力学。在这些简化的条件下,运动控制的基本要素,如身体力学、目标函数和感觉反馈的作用,已经得到了描述。然而,这些要素大多是孤立地进行研究的,它们之间的相互作用受到的关注较少。本研究考察了一个具有内部动力学的任务,该任务受到日常技能的启发,即运输一杯咖啡,同时对预期或意外的干扰进行额外的探测,以研究控制器的结构。使用最优反馈控制(OFC)作为基础,证明有必要赋予身体模型以机械阻抗,以产生在人类实验数据中观察到的运动学特征。添加机械阻抗表明,模拟运动不再对目标函数高度敏感,而目标函数是最优控制的一个极具争议的基石。此外,在处理干扰时,控制输入的前馈重放与包含反馈或感觉信息时同样成功。这些发现表明,当控制模型包含肢体机械特性的表示(即体现其动力学)时,特定的目标函数和感觉反馈就不那么关键了,并且可以成功地管理与动态物体的复杂相互作用。