Conventional design for robotics is based on the assumption that the robot should operate only in one given environment. As a result, often their skills are not transferable. Biological systems on the other hand are surprisingly versatile and robust. They exhibit remarkable adaptivity by placing more emphasis on adapting their morphology. Consequently, providing robots with mechanisms to adapt their bodies (material properties and even removing/adding parts) could be a way to obtain more versatile and robust systems. In this paper we propose a novel method which uses genetic algorithms to evolve optimal adaptation rules for changing the bodies of soft robots. Instead of optimising the morphology directly, we optimise the rules that tell the robot how to adapt the body based on the feedback it receives when interacting with the environment. It uses a combination of local and global information to sculpt (i.e., change stiffness and remove body parts) the soft body to improve locomotion in different environments. We show that in some cases the same rule with the same starting morphology can lead to different, but beneficial morphologies in different environments, i.e., it can translate feedback from the different environments into different useful bodily changes. Furthermore, we demonstrate that some of the found rules are highly robust and are able to produce successful morphologies for a range of environments that haven't been experienced during the optimisation process.

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