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Evolving Robotic Desires: A New Approach to Bridging the Reality Gap

Since the emergence of the field of Evolutionary Robotics, new breakthroughs have been made to further its development and prove its effectiveness. While from a purely engineering standpoint the modeling of biological evolutionary phenomena may not seem to be the most efficient means of implementation, it has proven to be able to provide solutions to interesting problems within the robotics world. The approach of evolving autonomous controllers for robots has had proven benefits when compared to a more traditional hand-coded approach. Like most fields of research, Evolutionary Robotics contains its own set of problems. One such problem involves the use of simulators to speed up the evolutionary processes. When transferring the robotic controller from the simulation to the physical robot it tends to perform poorly on a given task. This issue is referred to as the reality gap problem. In this paper, a new approach to bridging the reality gap is presented and explored. The goal is to evolve a robotic controller that gains correct desires based on its current state and uses reinforcement learning to perform actions that achieve such desires. By doing so, the goal is to have a robotic controller adapt to uncertainties and perturbations within the real world once transferred from simulation.