Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers

Understand

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.