A striking difference between animals and traditional robots is that the latter usually have rigid and non-flexible bodies. Animals, on the other hand, exhibit highly adapted traits, such as elastic tendons. The tendons work as springs, storing and releasing kinetic energy during an animal’s gait cycle. Springs have been used in some hand designed robots for similar benefits. However, little research has been done on springs in robots with evolving morphology. We examine the use of compliant and structural modules in modular robots, using a standard evolutionary algorithm. We also look at connections between spring stiffness and robot size using the quality diversity algorithm MAP-Elites. We found that the modular robots evolved to use elastic actuators, and that structural modules enabled morphologies that use less actuators, but still achieve the same walking speed as the robots with actuators in every module. We also observe some indications that larger robots may require lower elasticity.

One of the core functions in most Evolutionary Algorithms is mutation. In complex search spaces, which are common in Evolutionary Robotics, mutation is often used both for optimizing existing solutions, described as exploitation, and for spanning the search space, called exploration. This presents a difficult challenge for researchers as mutation parameters must be selected with care in order to balance the two, often contradictory, effects. Strategies that vary mutation during the search often try to estimate these effects in order to modify the mutation parameters. In this regard MAP-Elites, a Quality Diversity algorithm, presents an interesting opportunity. Because factors related to exploration and exploitation are readily available during the search, optimization based on these factors could be utilized to improve the search. In this paper we study how online adaptation of mutation rate, dynamic mutation, affects MAP-Elites in order to gain insight into how the search process is affected by the mutation rate. Our study compares fixed and dynamic mutation parameters for two different complex gait controllers. The results show that dynamic mutation combines favorably with MAP-Elites and that there is a strong relation between mutation parameters and exploration that can be utilized.

Internal models allow us to simulate and predict the consequences of interacting with the objects in our environment. Applying such models in intelligent robots and machines is a key challenge in increasing their autonomy, robustness and responsiveness. One obstacle in allowing this is the need to maintain multiple internal models, corresponding to the multitude of objects in our surroundings, without interference between them. We propose evolving neural networks as a way to generate multiple internal models, and study the role of neural modularity in doing so. Intuitively, modularity should help reduce interference between internal models. In a task requiring neural networks to control multiple different objects, we demonstrate that neuroevolution can produce multiple internal inverse models. Results indicate that modularity may play a role – but the evolved neural networks reveal an unexpected modular decomposition: Rather than separating models of different objects, networks frequently divide into modules separately processing different observed features of objects.