Emergent interactions lead to collective frustration in robotic matter

Abstract:Artificial intelligence and robotic systems are increasingly deployed as interacting collectives of learning agents. This raises the question of whether robotic matter, where many learning agents interact, shows the emergence of collective behaviour. Here we study a paradigmatic model of robotic matter and show the emergence of a range of complex, collective behaviours. Specifically, we study systems composed of stochastic interacting particles, each endowed with a deep neural network that optimises transitions based on its environment. In a one-dimensional system, we show that robotic matter exhibits complex phenomena arising from emergent interactions, including self-organisation into distinct temporal learning regimes, particle species, and long-lived frustrated states with suboptimal reward. We further identify an abrupt, density-dependent change in collective behaviour. Active matter theory suggests that this phenomenon reflects a phase transition with signatures of criticality. Our results establish robotic matter as a platform for novel non-equilibrium physics.

Soft Condensed Matter (cond-mat.soft); Robotics (cs.RO)

arXiv:2507.22148 [cond-mat.soft]

(or arXiv:2507.22148v3 [cond-mat.soft] for this version)

https://doi.org/10.48550/arXiv.2507.22148 Focus to learn more arXiv-issued DOI via DataCite

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