Demiris Yiannis, Simmons Gavin
Biologically Inspired Autonomous Robots Team (BioART), Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2BT, UK.
Neural Netw. 2006 Apr;19(3):272-84. doi: 10.1016/j.neunet.2006.02.005. Epub 2006 Apr 3.
Recent computational approaches to action imitation have advocated the use of hierarchical representations in the perception and imitation of demonstrated actions. Hierarchical representations present several advantages, with the main one being their ability to process information at multiple levels of detail. However, the nature of the hierarchies in these approaches has remained relatively unsophisticated, and their relation with biological evidence has not been investigated in detail, in particular with respect to the timing of movements. Following recent neuroscience work on the modulation of the premotor mirror neuron activity during the observation of unpredictable grasping movements, we present here an implementation of our HAMMER architecture using the minimum variance model for implementing reaching and grasping movements that have biologically plausible trajectories. Subsequently, we evaluate the performance of our model in matching the temporal dynamics of the modulation of cortical excitability during the passive observation of normal and unpredictable movements of human demonstrators.
近期用于动作模仿的计算方法主张在示范动作的感知和模仿中使用分层表示。分层表示具有多个优点，主要优点之一是它们能够在多个细节层次上处理信息。然而，这些方法中层次结构的本质仍然相对简单，并且它们与生物学证据的关系尚未得到详细研究，特别是在运动时间方面。继最近关于在观察不可预测的抓握动作期间前运动镜像神经元活动调制的神经科学研究之后，我们在此展示了我们的HAMMER架构的一种实现方式，该实现方式使用最小方差模型来实现具有生物学上合理轨迹的伸手和抓握动作。随后，我们评估了我们的模型在被动观察人类示范者的正常和不可预测动作期间匹配皮质兴奋性调制的时间动态方面的性能。
