Generalization in adaptation to stable and unstable dynamics.
Generalization in adaptation to stable and unstable dynamics.
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DOI：
10.1371/journal.pone.0045075
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Burdet E
中科院分区：
文献类型：
--
作者：
Kadiallah A;Franklin DW;Burdet E
尽管存在固有的不稳定性，人类仍能熟练地操纵物体和工具。为了成功完成这些任务，感觉运动控制系统必须建立力和机械阻抗的内部表示。由于为每一个可能的未来动作学习或存储运动命令是不现实的，感觉运动控制系统基于对一组动作进行的学习，为一系列动作推广了一种控制策略。在这里，我们为这种学习和泛化引入了一个计算模型，该模型指定了如何在状态空间的函数中学习前馈肌肉活动。具体来说，通过将共同激活作为误差函数纳入反馈命令，我们能够从梯度下降最小化运动误差和努力中推导出一种算法，并保持稳定裕度。该算法可用于学习协调各种运动原语中的任何一种，如力场、肌肉协同作用、物理模型或人工神经网络。该模型能够适应稳定和不稳定的动态，为机器人产生有效的自适应运动行为提供了一种控制器。模拟结果显示出与所有需要力和阻抗适应的新动力学学习实验一致的预测，并使我们能够重新审视以前对泛化实验的一些解释。
Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.
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