Jun 23, 2026 · cs.ROJ/K move · Enter open · S save
Paolo Leopardi, Heiko Hamann, Jonas Kuckling, Tanja Katharina Kaiser
Centre for the Advanced Study of Collective Behaviour & Department of Computer and Information Science University of Konstanz Konstanz, Germany · Department of Computer Science and Artificial Intelligence University of Technology Nuremberg Nuremberg, Germany
Task specialization can improve the efficiency of multi-robot systems (MRSs). Previous works have investigated the emergence of task-specialist robot controllers through evolutionary optimization and have argued that task specialization is more likely to evolve when subtask behaviors are readily available as building blocks. However, the available evaluation budget must be distributed across all subtasks, whereas a single generalist behavior can exploit the entire budget for its own optimization. We present a cost-benefit analysis of evolving task-specialist versus generalist behaviors in a foraging scenario here. In a physics-based robotics simulator, we study the total evaluation budget required to evolve task-specialist behaviors that outperform generalist behaviors across MRS sizes. We show that with increasing MRS size, a lower total evaluation budget is sufficient to evolve specialists that outperform generalists.