Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers
This addresses the reality gap issue in evolutionary robotics for UAVs, offering a novel approach to reduce reliance on high-accuracy simulations.
The paper tackled the problem of transferring controllers learned in simulation to reality for small UAVs by designing an evolved plastic spiking neurocontroller that adapts online, resulting in better performance than non-plastic controllers.
A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality. This is especially the case for small Unmanned Aerial Vehicles (UAVs), as the platforms are highly dynamic and susceptible to breakage. Previous approaches often require simulation models with a high level of accuracy, otherwise significant errors may arise when the well-designed controller is being deployed onto the targeted platform. Here we try to overcome the transfer problem from a different perspective, by designing a spiking neurocontroller which uses synaptic plasticity to cross the reality gap via online adaptation. Through a set of experiments we show that the evolved plastic spiking controller can maintain its functionality by self-adapting to model changes that take place after evolutionary training, and consequently exhibit better performance than its non-plastic counterpart.