The body of robots and their controllers need to be adapted to the task that they carry out. While it is possible to design and optimize free-form morphologies, its physical implementation consumes too many resources. In contrast, modular robots provide a feasible approach to design robotic morphologies that can be deployed in minutes, making them a suitable tool to implement virtual creatures. In this article, we tackle the main challenges to consider when evolving modular robots and mention some opportunities that these systems can provide.

Navigating the world is a fundamental ability for any living entity. Accomplishing the same degree of freedom in technology has proven to be difficult. The brain is the only known mechanism capable of voluntary navigation, making neuroscience our best source of inspiration toward autonomy. Assuming that state representation is key, we explore the difference in how the brain and the machine represent the navigational state. Where Reinforcement Learning (RL) requires a monolithic state representation in accordance with the Markov property, Neural Representation of Euclidean Space (NRES) reflects navigational state via distributed activation patterns. We show how NRES-Oriented RL (neoRL) agents are possible before verifying our theoretical findings by experiments. Ultimately, neoRL agents are capable of behavior synthesis across state spaces – allowing for decomposition of the problem into smaller spaces, alleviating the curse of dimensionality.

Reaction-Diffusion (RD) systems provide a computational framework that governs many pattern formation processes in nature. Current RD system design practices boil down to trial-and-error parameter search. We propose a differentiable optimization method for learning the RD system parameters to perform example-based texture synthesis on a 2D plane. We do this by representing the RD system as a variant of Neural Cellular Automata and using task-specific differentiable loss functions. RD systems generated by our method exhibit robust, non-trivial “life-like” behavior.

Moving around in the environment is a fundamental skill for mobile robots. This makes the evolution of an appropriate gait, a pivotal problem in evolutionary robotics. Whereas the majority of the related studies concern robots with predefined modular or legged morphologies and locomotion speed as the optimization objective, here we investigate robots with evolvable morphologies and behavioral traits included in the fitness function. To analyze the effects we consider morphological as well as behavioral features of the evolved robots. To this end, we introduce novel behavioral measures that describe how the robot locomotes and look into the trade-off between them. Our main goal is to gain insights into differences in possible gaits of modular robots and to provide tools to steer evolution towards objectives beyond ‘simple’ speed.

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.

In this work, we evolve phenotypically plastic robots - robots that adapt their bodies and brains according to environmental conditions - in changing environments. In particular, we investigate how the possibility of death in early environmental conditions impacts evolvability and robot traits. Our results demonstrate that early-death improves the efficiency of the evolutionary process for the earlier environmental conditions. On the other hand, the possibility of early-death in the earlier environmental conditions results in a dramatic loss of performance in the latter environmental conditions.