Evolving Robots on Easy Mode: Towards a Variable Complexity Controller for Quadrupeds
This work addresses the challenge of reducing optimization costs for gait evolution in robotics, offering a practical solution for adapting to varying task demands, though it is incremental in nature.
The paper tackles the problem of evolving specialized gaits for legged robots by introducing a variable complexity controller that adapts to different optimization budgets, showing that high complexity performs best with ample evaluations in simulation, while lower complexity is preferable for real-world tasks with limited budgets.
The complexity of a legged robot's environment or task can inform how specialised its gait must be to ensure success. Evolving specialised robotic gaits demands many evaluations - acceptable for computer simulations, but not for physical robots. For some tasks, a more general gait, with lower optimization costs, could be satisfactory. In this paper, we introduce a new type of gait controller where complexity can be set by a single parameter, using a dynamic genotype-phenotype mapping. Low controller complexity leads to conservative gaits, while higher complexity allows more sophistication and high performance for demanding tasks, at the cost of optimization effort. We investigate the new controller on a virtual robot in simulations and do preliminary testing on a real-world robot. We show that having variable complexity allows us to adapt to different optimization budgets. With a high evaluation budget in simulation, a complex controller performs best. Moreover, real-world evolution with a limited evaluation budget indicates that a lower gait complexity is preferable for a relatively simple environment.