Future-Aware Flow Planning for Safe UAV Target Following

arXiv:2609.10166 · cs.RO · Submitted 2026-09-09 · Read on arXiv

cs.RO

Submitted: 2026-09-09

Updated: 2026-09-21

Code: https://github.com/BoningFeng/Future-Aware-Flow-Planning

Terminology

• Benchmark — 21× in this paper · explained in 2 episodes
• Ablation — 16× in this paper · explained in 1 episode
• Robustness — 8× in this paper · explained in 5 episodes
• Flow Matching — 7× in this paper · explained in 8 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• International Conference on Robotics and Automation — 4× in this paper
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• International Conference on Unmanned Aircraft Systems — 2× in this paper
• Alignment — 1× in this paper · explained in 1 episode
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• Lyapunov function — 1× in this paper · explained in 2 episodes
• Motion Primitives — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes

Sources

• Planning with Diffusion for Flexible Behavior Synthesis
• Flow Matching for Generative Modeling
• Elucidating the Design Space of Diffusion-Based Generative Models
• Back to Basics: Let Denoising Generative Models Denoise
• L1 Sample Flow for Efficient Visuomotor Learning
• TNT: Target-driveN Trajectory Prediction
• SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
• SafeFlow: Safe Robot Motion Planning with Flow Matching via Control Barrier Functions
• SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions
• UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving