Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks
Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks
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DOI：
10.1155/2019/4862157
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发表时间：
2019-01-01
影响因子：
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通讯作者：
Pedrocchi, Alessandra
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文献类型：
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作者：
Antonietti, Alberto;Martina, Dario;Pedrocchi, Alessandra
一个生物启发的自适应模型，开发了一个尖峰神经网络由数千个人工神经元，已被利用来控制一个人形NAO机器人在真实的时间。该系统的学习特性在经典的小脑驱动范式（扰动的上肢到达协议）中受到了挑战。用于开发模型的神经生理学原理成功地驱动了具有基线、获取和消退阶段的自适应运动控制协议。尖峰神经网络模型显示出与人类受试者在获取阶段相同任务中实验测量的学习行为相似的学习行为，而在灭绝阶段则采用其他策略。该模型处理实时外部输入，编码为尖峰，并解码其输出神经元的生成尖峰活动，以提供对电机致动器的适当校正。针对不同的连接和不同的时间尺度，嵌入了三个双向长期塑性规则。可塑性塑造了网络输出层神经元的放电活动。在扰动上肢到达协议中，神经机器人成功地学会了如何补偿外部扰动，从而产生适当的校正。因此，尖峰小脑模型能够在机器人平台中再现生物系统如何在理想和真实的（噪音）环境中处理外部误差源。
A bioinspired adaptive model, developed by means of a spiking neural network made of thousands of artificial neurons, has been leveraged to control a humanoid NAO robot in real time. The learning properties of the system have been challenged in a classic cerebellum-driven paradigm, a perturbed upper limb reaching protocol. The neurophysiological principles used to develop the model succeeded in driving an adaptive motor control protocol with baseline, acquisition, and extinction phases. The spiking neural network model showed learning behaviours similar to the ones experimentally measured with human subjects in the same task in the acquisition phase, while resorted to other strategies in the extinction phase. The model processed in real-time external inputs, encoded as spikes, and the generated spiking activity of its output neurons was decoded, in order to provide the proper correction on the motor actuators. Three bidirectional long-term plasticity rules have been embedded for different connections and with different time scales. The plasticities shaped the firing activity of the output layer neurons of the network. In the perturbed upper limb reaching protocol, the neurorobot successfully learned how to compensate for the external perturbation generating an appropriate correction. Therefore, the spiking cerebellar model was able to reproduce in the robotic platform how biological systems deal with external sources of error, in both ideal and real (noisy) environments.