DLC-NddMode: A Spiking Neural Network Tactile Object Recognition Model With Adaptive Optimisation and Regularisation

Abstract

Empowering robots with tactile perception is crucial for the future development of intelligent robots. Tactile perception can expand the application scenarios of robots to perform more complex tasks. Unfortunately, existing approaches are flawed in their use of data collected by robotic tactile sensors because they either do not consider that tactile sensation is event-driven, which means that tactile data are spatiotemporal, or they ignore that too few samples of tactile data would cause overfitting problems in the network model. We introduce DLC-NddModel, a method based on spiking neural networks (SNNs) that incorporates Adam optimisation, regularisation and cosine annealing method. DLC-NddModel aims to fully interpret the spatiotemporal nature of the tactile data using the spatiotemporal dynamics of SNNs and to alleviate the overfitting problem caused by the few samples. Furthermore, unlike previous work using SNNs, we use a different approximation function to surmount the nondifferentiable spiking activity of the spiking neurons, thus making the gradient descent method usable and effective. To effectively alleviate the overfitting problem caused by too few tactile data samples, we explore solutions through regularisation strategies that add training noise or regularisation terms to the loss function. We compare DLC-NddModel against four prior state-of-the-art approaches on the EvTouch-Objects tactile spike dataset. Our experimental results demonstrate that DLC-NddModel has higher recognition accuracy than the comparison method when recognising household object data with an ACC value improvement of at least 2.362%.