# Evolving Robots on Easy Mode: Towards a Variable Complexity Controller for Quadrupeds

## TL;DR

This paper presents a variable complexity gait controller for quadruped robots that adapts to different optimization budgets, balancing simplicity and sophistication for effective locomotion in simulation and real-world scenarios.

## Contribution

Introduces a novel gait controller with adjustable complexity via a single parameter, enabling adaptable optimization for diverse tasks and evaluation constraints.

## Key findings

- Higher complexity controllers excel with ample evaluation budgets in simulation.
- Lower complexity gaits are more effective in simple real-world environments with limited evaluations.
- Variable complexity allows flexible adaptation to different optimization scenarios.

## Abstract

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.