Decentralized Innovation Marking for Neural Controllers in Embodied Evolution

International audienceWe propose a novel innovation marking method for Neuro-Evolution of Augmenting Topologies in Embodied Evolutionary Robotics. This method does not rely on a centralized clock, which makes it well suited for the decentralized nature of EE where no central evolutionary process governs the adaptation of a team of robots exchanging messages locally. This method is inspired from event dating algorithms, based on logical clocks, that are used in distributed systems, where clock synchronization is not possible. We compare our method to odNEAT, an algorithm in which agents use local time clocks as innovation numbers, on two multi-robot learning tasks: navigation and item collection. Our experiments showed that the proposed method performs as well as odNEAT, with the added benefit that it does not rely on synchronization of clocks and is not affected by time drifts

Learning Collaborative Foraging in a Swarm of Robots using Embodied Evolution

International audienceIn this paper, we study how a swarm of robots adapts over time to solve a collaborative task using a distributed Embodied Evolutionary approach , where each robot runs an evolutionary algorithm and they locally exchange genomes and fitness values. Particularly, we study a collabo-rative foraging task, where the robots are rewarded for collecting food items that are too heavy to be collected individually and need at least two robots to be collected. Further, the robots also need to display a signal matching the color of the item with an additional effector. Our experiments show that the distributed algorithm is able to evolve swarm behavior to collect items cooperatively. The experiments also reveal that effective cooperation is evolved due mostly to the ability of robots to jointly reach food items, while learning to display the right color that matches the item is done suboptimally. However, a closer analysis shows that, without a mechanism to avoid neglecting any kind of item, robots collect all of them, which means that there is some degree of learning to choose the right value for the color effector depending on the situation

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning in Evolutionary Robotics

Robot-to-robot learning, a specific case of social learning in robotics, enables multiple robots to share learned skills while completing a task. The literature offers various statements of its benefits. Robots using this type of social learning can reach a higher performance, an increased learning speed, or both, compared to robots using individual learning only. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this paper, we perform a detailed analysis into the effects of robot-to-robot learning. As a result, we show that this type of social learning can reduce the sensitivity of the learning process to the choice of parameters in two ways. First, robot-to-robot learning can reduce the number of bad performing individuals in the population. Second, robot-to-robot learning can increase the chance of having a successful run, where success is defined as the presence of a high performing individual. Additionally, we show that robot-to-robot learning results in an increased learning speed for almost all parameter settings. Our results indicate that robot-to-robot learning is a powerful mechanism which leads to benefits in both performance and learning speed