Dresden 2026 – scientific programme
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CPP: Fachverband Chemische Physik und Polymerphysik
CPP 18: Active Matter III (joint session BP/CPP/DY)
CPP 18.12: Talk
Tuesday, March 10, 2026, 12:30–12:45, BAR/SCHÖ
Emergent interactions lead to collective frustration in robotic matter — •Onurcan Bektas1,3, Adolfo Alsina2,3, and Steffen Rulands1,3 — 1Arnold-Sommerfeld-Center for Theoretical Physics and Center for NanoSciences, Ludwig-Maximilians-Universität München, Theresienstr. 37, 80333 München, Germany — 2GISC, Universidad Rey Juan Carlos, Tulipán, 28933, Móstoles, Spain — 3Max-Planck-Institute for the Physics of Complex Systems, Noethnitzer Str. 38, 01187 Dresden, Germany
Current artificial intelligence systems show near-human-level capabilities when deployed in isolation. Systems with intelligent agents are deployed to perform tasks collectively. This raises the question of whether robotic matter, where many learning and intelligent agents interact, shows emergence of collective behaviour. And if so, what kind of phenomena would such systems exhibit? Here, we study a paradigmatic model for robotic matter: a system composed of a large collection of stochastic interacting particles where each particle is endowed with a deep neural network that optimizes its transitions based on the particles' environments. For a 1D model, robotic matter exhibits complex phenomena arising from emergent interactions, including transitions between long-lived learning regimes, the emergence of particle species, and frustration. We also find an abrupt, density-dependent change in the behaviour of particles. Using active matter theory, we show that this phenomenon is a reflection of a phase transition with signatures of criticality. Our model captures key phenomena observed in more complex forms of robotic systems.
Keywords: robotic matter; emergent interactions; active matter