LATIDIA · Robótica

Evaluación de LLMs Multimodales como Agentes Generalistas de Visión-Lenguaje-Acción para el Control de Drones: Comando, Aproximación, Seguimiento y Búsqueda

arXiv:2609.01404v2 Tipo de anuncio: reemplazar Resumen: Los modelos multimodales de lenguaje grande (MLLM) se utilizan cada vez más para tomar decisiones para drones, pero las evaluaciones existentes se limitan principalmente a la simulación, a un solo dron

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arXiv:2609.01404v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are increasingly used to make decisions for drones, but existing evaluations are mostly limited to simulation, to a single drone, and to targets that are visible from the start and do not move. We present DroneCATS, a benchmark in which the MLLM alone controls the drone through four actions, and evaluate ten MLLMs from frontier models to small open models. The benchmark varies target visibility, target motion, scene and simulator, and extends to real drones. Beyond a single drone, it adds a multi-drone suite, where one model drives multiple drones, and a commander + sub-agents suite, where a commander model