Designing stable and robust controllers for multilegged robots is a challenging task, and it would be desirable to develop automated methods for doing it. Learning the control behavior is difficult however because optimal behavior is not known, and the search space is too large for reinforcement learning and for straightforward evolution. As a potential solution, this project uses an approach called Evolution of Network Symmetry and mOdularity (ENSO). ENSO generates appropriate gaits for a given robot and environment by utilizing a mathematical model of animal locomotion and group-theoretic analysis of controller symmetry. It represents the controllers as interconnected neural network modules and optimizes module functionality and interconnection symmetry using evolution. In this project, this approach is evaluated by evolving controllers for a quadruped robot in physically realistic simulations. On flat ground, the resulting controllers are as effective as those having hand-designed symmetries. However, they are significantly faster when evolved on inclined ground, where the appropriate symmetries are difficult to determine manually. The group-theoretic symmetry mutations of ENSO were also significantly more effective at evolving such controllers than random symmetry mutations. These results suggest that ENSO can be used to design effective locomotion controllers for multilegged robots in challenging environments.

Videos of evolved walking behaviors