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New Self-Specialization Algorithm Explored

The robots were tasked with completing a task similar to that performed by Atta leafcutter ants. (Photo credit: PLOS)
The robots were tasked with completing a task similar to that performed by Atta leafcutter ants. (Photo credit: PLOS)

New research suggests that teams of robots that self-specialize to perform specific tasks using a novel evolutionary method might have an advantage when completing missions. The study involved robotic simulations modeled loosely after the activity of leaf cutter ants that cut leaves off trees to use in their nests. Simulated MarXbots had to collect items, take them down a slope (which represented the tree scaled by real ants), and deposit them in a special spot representing the nest.

“One of the unsolved mysteries in biology is how a blind process of Darwinian selection could have led to such highly complex forms of sociality. To answer this question, we used simulated teams of robots and artificially evolved them to achieve maximum performance in a foraging task. We find that, as in social insects, this favored controllers that caused the robots to display a self-organized division of labor in which the different robots automatically specialized into carrying out different subtasks in the group,” read a paper describing the research that was recently published in PLOS Computational Biology.

“Remarkably, such a division of labor could be achieved even if the robots were not told beforehand how the global task of retrieving items back to their base could best be divided into smaller subtasks. This is the first time that a self-organized division of labor mechanism could be evolved entirely de-novo. In addition, these findings shed significant new light on the question of how natural systems managed to evolve complex sociality and division of labor.”

The work was completed by researchers at University of Leuven, the Free University of Brussels and the Middle East Technical University.

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