AirWorld: Language-Conditioned Predictive World Models for Generative UAV Exploration

We study open-vocabulary target search by a single micro aerial vehicle (UAV) instructed in natural language. Semantic exploration systems such as STEM propagate semantic priorities to frontier viewpoints and optimize a prescribed planning objective. We propose AirWorld, a language-conditioned predictive world model that maintains a belief over geometry, semantics, and task intention from RGB-D observations and UAV state. An action-conditioned latent model predicts future evidence, while a conditional flow-matching policy generates candidate control sequences. Belief-space rollouts rank candidates by discovery, coverage, expected information gain, and risk; a receding-horizon controller executes a short verified segment. We formulate the belief update, constrained trajectory generation, and training objectives, and report matched single-UAV benchmark results for the proposed method and the listed baselines.