Gait-learning with morphologically evolving robots generated by L-system
This work addresses a specific problem in evolutionary robotics by showing how learning can enhance morphological intelligence, though it is incremental as it builds on existing methods.
The researchers tackled the brain-body mismatch problem in evolving robots by comparing two methods: evolution-only and evolution plus learning (RevDEknn). They found that evolution plus learning led to higher fitness and more morphological evolution, demonstrating that brain changes can induce body changes, quantified by a learning delta.
When controllers (brains) and morphologies (bodies) of robots simultaneously evolve, this can lead to a problem, namely the brain & body mismatch problem. In this research, we propose a solution of lifetime learning. We set up a system where modular robots can create offspring that inherit the bodies of parents by recombination and mutation. With regards to the brains of the offspring, we use two methods to create them. The first one entails solely evolution which means the brain of a robot child is inherited from its parents. The second approach is evolution plus learning which means the brain of a child is inherited as well, but additionally is developed by a learning algorithm - RevDEknn. We compare these two methods by running experiments in a simulator called Revolve and use efficiency, efficacy, and the morphology intelligence of the robots for the comparison. The experiments show that the evolution plus learning method does not only lead to a higher fitness level, but also to more morphologically evolving robots. This constitutes a quantitative demonstration that changes in the brain can induce changes in the body, leading to the concept of morphological intelligence, which is quantified by the learning delta, meaning the ability of a morphology to facilitate learning.