Spiking neural network-based multi-task autonomous learning for mobile robots

Spiking Neural Networks (SNNs) are the new generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. In this work, a multi-task autonomous learning paradigm is proposed for the mobile robot application, which employs a SNN to construct the controlling system of the mobile robot. The Reward-modulated Spiking-time-dependent Plasticity learning rule is developed for the SNN-based controller, which aims to achieve the capability of autonomous learning under multiple tasks. Reward signals are generated based on the instantaneous frequencies of pre- and post-synaptic spikes, which adapts to the sensory stimuli and environmental feedback. Meanwhile, inspired by lateral inhibition connections, a task switch mechanism is designed to enable the controller to switch the operations between multiple tasks. Two tasks of obstacle avoidance and target tracking are used for performance evaluation and results demonstrate that the mobile robot with the proposed paradigm is able to autonomously learn, switch and complete the tasks.

Automated summary

Spiking Neural Networks (SNNs) mimic the time encoding and information processing aspects of the human brain. A multi-task autonomous learning paradigm is proposed for mobile robot applications using SNNs, with a focus on developing a Reward-modulated Spiking-time-dependent Plasticity learning rule.