Comparison of Selection Methods in On-line Distributed Evolutionary Robotics

In this paper, we study the impact of selection methods in the context of on-line on-board distributed evolutionary algorithms. We propose a variant of the mEDEA algorithm in which we add a selection operator, and we apply it in a taskdriven scenario. We evaluate four selection methods that induce different intensity of selection pressure in a multi-robot navigation with obstacle avoidance task and a collective foraging task. Experiments show that a small intensity of selection pressure is sufficient to rapidly obtain good performances on the tasks at hand. We introduce different measures to compare the selection methods, and show that the higher the selection pressure, the better the performances obtained, especially for the more challenging food foraging task

Embedded harmonic control for dynamic trajectory planning on

This paper presents a parallel hardware implementation of a well-known navigation control method on reconfigurable digital circuits. Trajectories are estimated after an iterated computation of the harmonic functions, given the goal and obstacle positions of the navigation problem. The proposed massively distributed implementation locally computes the direction to choose to get to the goal position at any point of the environment. Changes in this environment may be immediately taken into account, for example when obstacles are discovered during an on-line exploration. The implementation results show that the proposed architecture simultaneously improves speed, power consumption, precision, and environment size.

Learning environment dynamics from self-adaptation. A preliminary investigation

We present an experimental study that shows a relationship between the dynamics of the environment and the adaptation of strategy parameters. Experiments conducted on two adaptive evolutionary strategies SA-ES and CMA-ES on the dynamic sphere function, show that the nature of the movements of the function's optimum are reflected in the evolution of the mutation steps. Three types of movements are presented: constant, linear and quadratic velocity, in all, the evolution of mutation steps during adaptation reflect distinctly the nature of the movements. Furthermore with CMA-ES, the direction of movement of the optimum can be extracted

How to design good Tetris players

In this paper, we propose to use evolution- nary algorithms more specifically the covariance matrix adaptation evolution strategy to design artificial players for the game of Tetris. The learned strategies are among the best performing players at this time scoring several millions of lines. We also describe different mechanisms to reduce the evolution time which can be an important issue for this learning problem

Promoting Reproductive Isolation Through Diversity in On-line Collective Robotics

International audienceWe present a behavioral diversity selection scheme that favors reproductive isolation to promote the learning of multiple task in on-line embodied evolutionary robotics (EER). The scheme estimates the behavior of the controllers without the need to access the agent experience, respecting thus the online, distributed properties EER. Reproductive isolation is assessed through coalescence trees and task specialization is tested on a concurrent foraging setting

When Mating Improves On-line Collective Robotics

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Phylogeny of Embodied Evolutionary Robotics

International audienceWe explore the idea of analyzing EER algorithms from a gene perspective using phylogenetic trees. We illustrate a general approach on a simple question and argue that this type of approach could help understand these algorithms differently

Seeking Specialization Through Novelty in Distributed Online Collective Robotics

International audienceOnline Embodied Evolution is a distributed learning method for collective heterogeneous robotic swarms, in which evolution is carried out in a decentralized manner. In this work, we address the problem of promoting reproductive isolation, a feature that has been identified as crucial in situations where behavioral specialization is desired. We hypothesize that one way to allow a swarm of robots to specialize on different tasks is through the promotion of diversity. Our contribution is twofold, we describe a method that allows a swarm of heterogeneous agents evolving online to maintain a high degree of diversity in behavioral space in which selection is based on originality. We also introduce a behavioral distance measure that compares behaviors in the same conditions to provide reliable measurements in online distributed situations. We test the hypothesis on a concurrent foraging task and the experiments show that diversity is indeed preserved and, that different behaviors emerge in the swarm; suggesting the emergence of reproductive isolation. Finally, we employ different analysis tools from computational biology that further support this claim

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

Embedded harmonic control for dynamic trajectory planning on FPGA

International audienceThis paper presents a parallel hardware implementation of a well-known navigation control method on reconfigurable digital circuits. Trajectories are estimated after an iterated computation of the harmonic functions, given the goal and obstacle positions of the navigation problem. The proposed massively distributed implementation locally computes the direction to choose to get to the goal position at any point of the environment. Changes in this environment may be immediately taken into account, for example when obstacles are discovered during an on-line exploration. The implementation results show that the proposed architecture simultaneously improves speed, power consumption, precision, and environment size