Paper 70 Details


Learning Collaborative Foraging in a Swarm of Robots using Embodied Evolution


Iñaki Fernández Pérez, Amine Boumaza, François Charpillet


Date: Thursday 7 Sept
Talk Time: TBA
Session: Collective dynamics of swarms 1 14:00


Embodied Evolution, Swarm Robotics, Cooperation, Neuroevolution


In 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 collaborative 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.

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