colep-certifiably-robust-learning-reasoning-conformal-prediction-framework-via-probabilistic-circuits

hierarchical-neurosymbolic-approach-for-action-quality-assessment-at-princeton-university

有限角度トモグラフィにおけるデータ駆動アプローチの堅牢性

addressing-the-reversal-curse-in-language-models-through-reverse-training

革新的なgnnモデルによるhsqcスペクトル予測の効果的なアプローチ

depthfm-fast-monocular-depth-estimation-with-flow-matching

グラフ理論による同意管理-複雑なデータフローの新しいアプローチ

igann-sparse-bridging-sparsity-and-interpretability-in-machine-learning-models


Constraint-Guided Diffusion (CGD) is a novel imitation learning-based approach that efficiently generates collision-free and dynamically feasible UAV trajectories by combining a diffusion policy with a surrogate optimization problem.


coremsg

constraint-guided-diffusion-policies-for-efficient-and-safe-uav-trajectory-planning


Constraint-Guided Diffusion Policies for Efficient and Safe UAV Trajectory Planning


title_rewrite

UAV Trajectory Planning