Chhabra Manu, Jacobs Robert A
Department of Computer Science, University of Rochester, Rochester, NY 14627, USA.
Neural Comput. 2006 Oct;18(10):2320-42. doi: 10.1162/neco.2006.18.10.2320.
We consider the properties of motor components, also known as synergies, arising from a computational theory (in the sense of Marr, 1982) of optimal motor behavior. An actor's goals were formalized as cost functions, and the optimal control signals minimizing the cost functions were calculated. Optimal synergies were derived from these optimal control signals using a variant of nonnegative matrix factorization. This was done using two different simulated two--joint arms--an arm controlled directly by torques applied at the joints and an arm in which forces were applied by muscles--and two types of motor tasks-reaching tasks and via-point tasks. Studies of the motor synergies reveal several interesting findings. First, optimal motor actions can be generated by summing a small number of scaled and time-shifted motor synergies, indicating that optimal movements can be planned in a low-dimensional space by using optimal motor synergies as motor primitives or building blocks. Second, some optimal synergies are task independent--they arise regardless of the task context-whereas other synergies are task dependent--they arise in the context of one task but not in the contexts of other tasks. Biological organisms use a combination of task--independent and task--dependent synergies. Our work suggests that this may be an efficient combination for generating optimal motor actions from motor primitives. Third, optimal motor actions can be rapidly acquired by learning new linear combinations of optimal motor synergies. This result provides further evidence that optimal motor synergies are useful motor primitives. Fourth, synergies with similar properties arise regardless if one uses an arm controlled by torques applied at the joints or an arm controlled by muscles, suggesting that synergies, when considered in "movement space," are more a reflection of task goals and constraints than of fine details of the underlying hardware.
我们研究了运动组件的特性，这些组件也被称为协同作用，它们源自一种关于最优运动行为的计算理论（按照Marr在1982年所定义的意义）。将参与者的目标形式化为成本函数，并计算出使成本函数最小化的最优控制信号。使用非负矩阵分解的一种变体，从这些最优控制信号中导出最优协同作用。这一过程通过两种不同的模拟双关节手臂来完成——一种是由施加在关节处的扭矩直接控制的手臂，另一种是由肌肉施加力的手臂——以及两种类型的运动任务——伸手任务和过点任务。对运动协同作用的研究揭示了几个有趣的发现。首先，通过对少量缩放和时移的运动协同作用进行求和，可以生成最优的运动动作，这表明通过将最优运动协同作用作为运动原语或构建块，可以在低维空间中规划最优运动。其次，一些最优协同作用与任务无关——无论任务背景如何都会出现——而其他协同作用则与任务相关——它们在一种任务背景下出现，但在其他任务背景下不出现。生物有机体使用任务无关和任务相关协同作用的组合。我们的工作表明，这可能是一种从运动原语生成最优运动动作的有效组合。第三，通过学习最优运动协同作用的新线性组合，可以快速获得最优运动动作。这一结果进一步证明了最优运动协同作用是有用的运动原语。第四，无论使用由施加在关节处的扭矩控制的手臂还是由肌肉控制的手臂，都会出现具有相似特性的协同作用，这表明当在“运动空间”中考虑协同作用时，它们更多地反映了任务目标和约束，而不是底层硬件的细微细节。
