Aerial World Model for Long-horizon Visual Generation and Navigation in 3D Space
This work addresses autonomous navigation for unmanned aerial vehicles by incorporating semantic planning, though it appears incremental as it builds on existing world model approaches with a physics-inspired module.
The authors tackled the problem of UAV navigation lacking high-level semantic planning by proposing ANWM, an aerial navigation world model that predicts future visual observations to rank trajectories by semantic plausibility. The model significantly outperformed existing world models in long-distance visual forecasting and improved UAV navigation success rates in large-scale environments.
Unmanned aerial vehicles (UAVs) have emerged as powerful embodied agents. One of the core abilities is autonomous navigation in large-scale three-dimensional environments. Existing navigation policies, however, are typically optimized for low-level objectives such as obstacle avoidance and trajectory smoothness, lacking the ability to incorporate high-level semantics into planning. To bridge this gap, we propose ANWM, an aerial navigation world model that predicts future visual observations conditioned on past frames and actions, thereby enabling agents to rank candidate trajectories by their semantic plausibility and navigational utility. ANWM is trained on 4-DoF UAV trajectories and introduces a physics-inspired module: Future Frame Projection (FFP), which projects past frames into future viewpoints to provide coarse geometric priors. This module mitigates representational uncertainty in long-distance visual generation and captures the mapping between 3D trajectories and egocentric observations. Empirical results demonstrate that ANWM significantly outperforms existing world models in long-distance visual forecasting and improves UAV navigation success rates in large-scale environments.