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Comparative Study of Multi-Robot Task Suitability Methods Accepted to IROS 2026
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Daniel Weiner and Raj Korpan compared 10 deterministic methods and 2 large language model (LLM)-based variants for heterogeneous multi-robot task allocation, covering suitability definitions based on rules, similarity, demand, fuzzy, and semantics, among others. The paper was accepted to the FM-MRS Workshop at IROS 2026. The results show that under the same scenarios, the methods often disagree on the scores and rankings of candidate robots; general similarity metrics may violate the suitability property of one-sided demand satisfaction, while LLM scoring introduces randomness and language sensitivity.
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- Robot-Task Suitability in Heterogeneous Multi-Robot Task Allocation
This study compares 10 deterministic methods and 2 LLM-based variants for heterogeneous multi-robot task allocation, covering rule-based, similarity-based, requirement-oriented, fuzzy, and semantic definitions of suitability. The results show that the methods often disagree on scoring and ranking candidate robots in the same scenario, that generic similarity measures can violate the suitability property of one-sided requirement satisfaction, and that LLM scoring introduces stochasticity and linguistic sensitivity. The paper was accepted to the FM-MRS Workshop at IROS 2026.
arXiv cs.RO ·