Minimize Surprise in Robots: An Innate Motivation for Collective Behavior

After a quick overview of other related research projects in my lab (bio-hybrid systems, swarm performance, collective decision-making), I will present our work on minimize surprise for multi-robot systems. Each robot has two artificial neural networks, a world model (“prediction machine”) and a behavioral module (“action selection network”), that are trained concurrently. There is no predefined task, instead the swarm is rewarded for making correct predictions about future sensory input. As an effect, robots discover behaviors introducing predictable spatiotemporal sensor patterns. I will present simulated results for flocking, aggregation, self-assembly, construction, and first results using real-world mobile robots.

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