Spiking Neural Network Robot Tactile Object Recognition Method with Regularization Constraints

It is important for the future development of intelligent robots to expand tactile perception ability, which determines the scope of application scenarios for robots. Tactile data collected by tactile sensors are the basis of robotics work, but these data have complex spatio-temporal properties. Spiking neural network has rich spatio-temporal dynamics and event-driven nature. It can better process spatio-temporal information and be applied to artificial intelligence chips to bring higher energy efficiency to robots. To solve the problem of backpropagation failure in the network training process caused by the discreteness of neuron spike activity in the spiking neural network, from the perspective of the dynamic system of the intelligent robot, the spiking activity approximation function is introduced to make the spiking neural network back-propagation gradient descent method effective. The over-fitting problem caused by the small amount of tactile data is alleviated by the regularization methods. Finally, the spiking neural network robot tactile object recognition algorithm SnnTd and SnnTdlc with regularization constraints are proposed. Compared with the classical methods TactileSGNet, Grid-based CNN, MLP and GCN, the SnnTd method tactile object recognition rate is improved by 5.00% over the best method TactileSGNet on EvTouch-Containers dataset, and the SnnTdlc method tactile object recognition rate is improved by 3.16% over the best method TactileSGNet on EvTouch-Objects dataset.