Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system
This work addresses the challenge of translating biological intelligence to robotics for real-world spatial mapping applications, though it appears incremental as it builds on existing neuroscience advancements.
The researchers tackled the problem of autonomous robot navigation by developing a spiking neural network (SNN) that mimics the brain's navigational system, enabling a robot to robustly map and explore unknown environments while compensating for hardware imperfections like loss of visual input.
It is true that the "best" neural network is not necessarily the one with the most "brain-like" behavior. Understanding biological intelligence, however, is a fundamental goal for several distinct disciplines. Translating our understanding of intelligence to machines is a fundamental problem in robotics. Propelled by new advancements in Neuroscience, we developed a spiking neural network (SNN) that draws from mounting experimental evidence that a number of individual neurons is associated with spatial navigation. By following the brain's structure, our model assumes no initial all-to-all connectivity, which could inhibit its translation to a neuromorphic hardware, and learns an uncharted territory by mapping its identified components into a limited number of neural representations, through spike-timing dependent plasticity (STDP). In our ongoing effort to employ a bioinspired SNN-controlled robot to real-world spatial mapping applications, we demonstrate here how an SNN may robustly control an autonomous robot in mapping and exploring an unknown environment, while compensating for its own intrinsic hardware imperfections, such as partial or total loss of visual input.