Modularized genotype combination to design multiobjective soft-bodied robots

The evolutionary method is an approach to the difficulties of designing soft-bodied robots. One of the prominent methods is compositional pattern producing network with neuroevolution of augmenting topologies (CPPN-NEAT). However, previous research has focused on single-function robots, and the design of multi-functional robots is still unsolved. This study provides a method for generating multi-functional robots by combining the genotype networks of single-functional robots in a modular manner. The proposed method includes the addition of a weight layer during network combination and the selection of populations with a fitness estimator. We conducted experiments to design voxel-based creatures that can perform two types of tasks in the simulation. Target tasks include terrestrial and aquatic locomotion. The results show that the proposed method was able to search for a form that satisfied the two tasks simultaneously faster than the existing methods. Observations of the generated populations indicated that the proposed method enables the efficient exploration of body morphology. Further, a modularized combination helps focus the exploration in a feasible morphology space. Finally, we fabricated evolved soft creatures in the real world as soft-bodied robots by limiting the arrangement of actuation voxels. We believe that the proposed method of designing a multi-functional robot while utilizing existing single-functional robots will contribute to the automatic design of multi-functional soft robots.