Integrating a hippocampus memory model into a neuromorphic robotic-arm for trajectory navigation

Abstract

Neuromorphic engineering endeavors to integrate the computational prowess and efficiency inherent in biological neuronal systems, such as the brain, into contemporary technological systems, primarily through the deployment of spiking neural networks. This research delineates the development and implementation of a bio-inspired sequential hippocampus memory model, which can effectively learn and sequentially recall memories, within a robotic infrastructure. The hippocampus memory model, implemented on the SpiNNaker platform, has been tactically utilized to control a 4-joint event-based robot arm, the ED-ScorBot, by learning and then recalling trajectories via a sequence of memories regarding joint positions. The conveyed spiking information from SpiNNaker is interpreted by an FPGA in real-time to command the event-driven motors of the robotic arm, integrating learned trajectories into physical robotic movement. An empirical exploration validates the model’s capability to govern the robotic arm’s trajectory with precision and dependability while simultaneously demonstrating the potential for incorporating spike-based memory models in robotic applications. This synergistic convergence of neuromorphic engineering and robotics illustrates a viable pathway towards sophisticated, efficient, and adaptable robotic systems capable of learning and reproducing complex tasks, with significant implications for future developments in autonomous robotic applications.