More complex environments may be required to discover benefits of lifetime learning in evolving robots
This work addresses the need for complex environments to properly evaluate learning benefits in evolutionary robotics, though it is incremental as it builds on known principles.
The study investigated the benefits of intra-life learning for evolving robot morphologies by comparing performance in flat versus hilly environments, finding that learning significantly improved results in the more challenging hills environment.
It is well known that intra-life learning, defined as an additional controller optimization loop, is beneficial for evolving robot morphologies for locomotion. In this work, we investigate this further by comparing it in two different environments: an easy flat environment and a more challenging hills environment. We show that learning is significantly more beneficial in a hilly environment than in a flat environment and that it might be needed to evaluate robots in a more challenging environment to see the benefits of learning.