Accelerating Soft Robot Evolution Using N-gram-based Controller Inheritance and Genetic Co-Design

Abstract

Soft robots have gain increasing attention due to their flexible morphologies suited for complex tasks in dynamic environments. However, the co-design of structure and control remains challenging due to the large search space and high demand of computational costs. To tackle this challenge, we proposed an N-gram-based controller inheritance framework that integrates genetic algorithms for structural evolution with Proximal Policy Optimization (PPO) for training controllers. The method captures sequential behavioral patterns from multiple ancestor policies and reuses them across generations to reduce redundant learning progress. Experimental results in the EvoGym benchmark show faster coveragence and improved final fitness compared to a non-inheritance baseline. Our approach provides a scalable framework for exploring policy inheritance in the evolutionary co-design of soft robots.