One of the major challenges of evolutionary robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction of the sensory inputs and motor actions as a tool to tackle this problem. Abstraction in robots is simply the use of preprocessed sensory inputs and low-level closed-loop control systems that execute higher-level motor commands. To demonstrate the impact abstraction could have, we evolved two controllers with different levels of abstraction to solve a task of forming an asymmetric triangle with a homogeneous swarm of micro air vehicles. The results show that although both controllers can effectively complete the task in simulation, the controller with the lower level of abstraction is not effective on the real vehicle, due to the reality gap. The controller with the higher level of abstraction is, however, effective both in simulation and in reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Additionally, abstraction aided in reducing the computational complexity of the simulation environment, speeding up the optimization process. Preeminently, we show that the optimized behavior exploits the environment (in this case the identical behavior of the other robots) and performs input shaping to allow the vehicles to fly into and maintain the required formation, demonstrating clear sensory-motor coordination. This shows that the power of the genetic optimization to find complex correlations is not necessarily lost through abstraction as some have suggested.

Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this article we show the first application of the Behavior Tree framework on a real robotic platform using the evolutionary robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behavior over that of the traditional neural network formulation. As a result, the behavior is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-g DelFly Explorer flapping wing micro air vehicle equipped with a 4-g onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the optimized behavior in simulation is 88%, and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.