Evaluating Multimodal LLMs as Generalist Vision-Language-Action Agents for Drone Control: Commanding, Approaching, Tracking and Searching
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 directs one sub-agent for each of the dro...
| Authors | Jaewoo Park, Minyoung Lee, Sukmin Seo et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 September 2026 |
| Last updated at source | 15 October 2026 |
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