The Evaluation Cost of Task Specialization in Evolutionary Multi-Robot Systems

Paolo Leopardi; Heiko Hamann; Jonas Kuckling; Tanja Katharina Kaiser

cs.ROJun 23, 2026

The Evaluation Cost of Task Specialization in Evolutionary Multi-Robot Systems

Authors: Paolo Leopardi, Heiko Hamann, Jonas Kuckling, Tanja Katharina Kaiser

Organizations: 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

Abstract

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.

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Varying Bundle Size Reactive Multi-Task Assignment using Selective Cost Estimation for Multi-Agent Systems

Niklas Dahlquist, Shridhar Velhal, George Nikolakopoulos

Luleå University of Technology, Luleå, SE-971 87 Sweden

This paper presents a scalable framework for multi-robot task allocation in complex environments where estimating task execution costs is computationally expensive. While combinatorial auction-based approaches offer reliable solutions, the exponential complexity of bundle generation typically renders them intractable for real-time reactive applications, particularly when accurate path planning is required for cost validation. We address this through a distributed, two-stage multi-fidelity bundle generation approach. Agents utilize a local search tree guided by a low-fidelity heuristic (such as euclidean distance) to rapidly explore the bundle space, applying high-fidelity path planning only to the most promising candidates in a best-first manner. These refined bids are then submitted to a central coordinator that solves a set packing problem to ensure global feasibility and maximize the overall utility. Simulation results in multiple environments demonstrate that the framework is able to improve the performance of reactive auction-based task allocation. Overall, the presented framework is shown to enable reactive task allocation with dynamic bundle sizes in multiple settings without exposing the agents' state and internal cost estimation models.

Multi-Robot Systems Patient-To-Room Assignment Problem Multi-Agent Systems Coverage Path Planning First-Price Auctions

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Abstract

Explore similar work

Varying Bundle Size Reactive Multi-Task Assignment using Selective Cost Estimation for Multi-Agent Systems

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