  In this paper, we propose an evolutionary predatorprey robot system which can
be generally implemented from simulation to the real world. We design the
closed-loop robot system with camera and infrared sensors as inputs of
controller. Both the predators and prey are co-evolved by NeuroEvolution of
Augmenting Topologies (NEAT) to learn the expected behaviours. We design a
framework that integrate Gym of OpenAI, Robot Operating System (ROS), Gazebo.
In such a framework, users only need to focus on algorithms without being
worried about the detail of manipulating robots in both simulation and the real
world. Combining simulations, real-world evolution, and robustness analysis, it
can be applied to develop the solutions for the predator-prey tasks. For the
convenience of users, the source code and videos of the simulated and real
world are published on Github.
