2019/02/12 by Tønnes Frostad Nygaard, Tønnes F. Nygaard, Charles Patrick Martin +8
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #FOS: Computer and information sciences #Genetic Mapping and Diversity in Plants and Animals #Genetics and Physical Performance #Robotic Locomotion and Control #Robotics (cs.RO) #cs.RO
paper · pdf · doi:10.48550/arxiv.1902.04403
Accepted to EvoApplications19
arxiv created 2019/02/12 · openalex publication_date 2019/02/12 · arxiv updated 2019/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.