Quality-Diversity algorithms search for large collections of diverse and high-performing solutions, rather than just for a single solution like typical optimisation methods. They are specially adapted for multi-modal problems that can be solved in many different ways, such as complex reinforcement learning or robotics tasks. However, these approaches are highly dependent on the choice of feature descriptors (FDs) quantifying the similarity in behaviour of the solutions. While FDs usually needs to be hand-designed, recent studies have proposed ways to define them automatically by using feature extraction techniques, such as PCA or Auto-Encoders, to learn a representation of the problem from previously explored solutions. Here, we extend these approaches to more complex problems which cannot be efficiently explored by relying only on a single representation but require instead a set of diverse and complementary representations. We describe MC-AURORA, a Quality-Diversity approach that optimises simultaneously several collections of solutions, each with a different set of FDs, which are, in turn, defined automatically by an ensemble of modular auto-encoders. We show that this approach produces solutions that are more diverse than those produced by single-representation approaches.

This paper deals with the programmability of a swarm of bio-micro-robots in order to display self-assembling behaviors into specific shapes. We consider robots that are DNA-functionalized micro-beads capable of sensing and expressing signals as well as self-assembling. We describe an in vitro experimentation with a million of micro-beads conditionally aggregating into clusters. Using a realistic simulation, we then address the question of how to automatically design the reaction networks that define the micro-robots' behavior, to self-assemble into a specific shape at a specific location. We use bioNEAT, an instantiation of the famous NEAT algorithm capable of handling chemical reaction networks, and CMA-ES to optimize the behavior of each micro-bead. As in swarm robotics, each micro-bead shares the same behavioral rules and the general outcome depends on interactions between neighbors and with the environment. Results obtained on four different target functions show that solutions optimized with evolutionary algorithms display efficient self-assembling behaviors, improving over pure hand-designed networks provided by an expert after a week-long trials and errors search. In addition, we show that evolved solutions are able to self-repair after damage, which is a critical property for smart materials.

Bio-mimetic robots can interact with groups of animals in bio-hybrid systems to study their behaviour by producing calibrated stimuli and by analysing their responses. Integrating a group of robots into a group of animals to mimic their behaviour is challenging, both in terms of robotic hardware design and robot control. In particular, the robots must be able to react in real-time to the animal changes of behaviour. This implies the need to adequately track and identify animal behaviour. In this paper, we present a novel framework to control several bio-mimetic robots to integrate into groups of fish. Our framework is able to track the position of the fish and robots in real-time. The robots are driven by a bio-mimetic model of fish behaviour from the literature. We show that our multi-robot system can successfully integrate into groups of fish with closed-loop interactions between the robots and the fish.

This paper investigates the properties required to evolve Artificial Neural Networks for distributed control in modular robotics, which typically involves non-linear dynamics and complex interactions in the sensori-motor space. We investigate the relation between macro-scale properties (such as modularity and regularity) and micro-scale properties in Neural Network controllers. We show how neurons capable of multiplicative-like arithmetic operations may increase the performance of controllers in several ways whenever challenging control problems with non-linear dynamics are involved. This paper provides evidence that performance and robustness of evolved controllers can be improved by a combination of carefully chosen micro- and macro-scale neural network properties.