Skytopia: Monocular Drone Navigation with Action-Conditioned Latent World Models

arXiv:2609.26007 · cs.RO, cs.AI · Submitted 2026-09-22 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-09-22

Updated: 2026-09-22

Project page: https://eugshang.github.io/Skytopia

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 34× in this paper · explained in 7 episodes
• 3D Gaussian Splatting — 5× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Adaptation — 5× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Gaussian Splatting — 5× in this paper · explained in 4 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• In Conference on Robot Learning — 3× in this paper
• Action Chunking — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Latent World Models — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Time to Success — 1× in this paper
• Unified Framework — 1× in this paper · explained in 5 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Qwen3-VL Technical Report
• NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance
• Policy-Guided World Model Planning for Language-Conditioned Visual Navigation
• ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics
• AstraNav-World: World Model for Foresight Control and Consistency
• Language-Conditioned World Modeling for Visual Navigation
• OmniVLA: An Omni-Modal Vision-Language-Action Model for Robot Navigation
• World Model for Robot Learning: A Comprehensive Survey
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• AirDreamer: Generalist Drone Navigation with World Models
• WAM-Nav: Asymmetric Latent World-Action Modeling for Unified Visual Navigation
• Sparse Video Generation Propels Real-World Beyond-the-View Vision-Language Navigation
• FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation
• RAE-NWM: Navigation World Model in Dense Visual Representation Space
• Hierarchical Planning with Latent World Models
• Grounded Vision-Language Navigation for UAVs with Open-Vocabulary Goal Understanding
• WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation
• Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

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