  Humans and animals are capable of quickly learning new behaviours to solve
new tasks. Yet, we often forget that they also rely on a highly specialized
morphology that co-adapted with motor control throughout thousands of years.
Although compelling, the idea of co-adapting morphology and behaviours in
robots is often unfeasible because of the long manufacturing times, and the
need to re-design an appropriate controller for each morphology. In this paper,
we propose a novel approach to automatically and efficiently co-adapt a robot
morphology and its controller. Our approach is based on recent advances in deep
reinforcement learning, and specifically the soft actor critic algorithm. Key
to our approach is the possibility of leveraging previously tested morphologies
and behaviors to estimate the performance of new candidate morphologies. As
such, we can make full use of the information available for making more
informed decisions, with the ultimate goal of achieving a more data-efficient
co-adaptation (i.e., reducing the number of morphologies and behaviors tested).
Simulated experiments show that our approach requires drastically less design
prototypes to find good morphology-behaviour combinations, making this method
particularly suitable for future co-adaptation of robot designs in the real
world.
