The central nervous system stabilizes unstable dynamics by learning optimal impedance
The central nervous system stabilizes unstable dynamics by learning optimal impedance
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DOI：
10.1038/35106566
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发表时间：
2001-11-22
期刊：
影响因子：
64.8
通讯作者：
Kawato, M
中科院分区：
文献类型：
--
作者：
Burdet, E;Osu, R;Kawato, M
为了操纵物体或使用工具，我们必须补偿与物理环境相互作用产生的任何力。最近的研究表明，这种补偿是通过学习动力学的内部模型来实现的（1-6），即运动命令和运动之间关系的神经表示（5，7）。在这些研究中，与物理环境的相互作用是稳定的，但许多常见的任务本质上是不稳定的（8，9）。例如，将螺丝刀保持在螺钉的狭槽中是不稳定的，因为平行于狭槽的过大的力可能导致螺丝刀滑动，并且因为方向错误的力可能导致螺丝刀和螺钉之间失去接触。稳定性可能取决于人手臂中的机械阻抗的控制，因为机械阻抗可以产生抵抗不稳定运动的力。在这里，我们研究了手臂运动在一个不稳定的动态环境中创建的机器人接口。我们的研究结果表明，人类学会稳定不稳定的动态使用巧妙和节能的策略选择性控制阻抗的几何形状。
To manipulate objects or to use tools we must compensate for any forces arising from interaction with the physical environment. Recent studies indicate that this compensation is achieved by learning an internal model of the dynamics(1-6), that is, a neural representation of the relation between motor command and movement(5,7). In these studies interaction with the physical environment was stable, but many common tasks are intrinsically unstable(8,9). For example, keeping a screwdriver in the slot of a screw is unstable because excessive force parallel to the slot can cause the screwdriver to slip and because misdirected force can cause loss of contact between the screwdriver and the screw. Stability may be dependent on the control of mechanical impedance in the human arm because mechanical impedance can generate forces which resist destabilizing motion. Here we examined arm movements in an unstable dynamic environment created by a robotic interface. Our results show that humans learn to stabilize unstable dynamics using the skilful and energy-efficient strategy of selective control of impedance geometry.