HumanFlow: 拡散駆動による人間追跡・予測・制御の密結合で実現するMAVの人間環境ナビゲーション
HumanFlow -- Diffusion-Driven MAV Navigation Among Humans via Tightly-Coupled Motion Tracking, Forecasting, and Control
3Dシーン文脈を条件とした潜在拡散モデルで人間の動作追跡と予測を統合し、重い遮蔽下でも高精度・高効率な予測を実現。さらに、その潜在表現をフローマッチングに基づく近似MPCポリシーに密結合し、実人間軌跡を用いたシミュレーションでMAVの社会的ナビゲーション性能を向上させた。
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著者: Simon Schaefer, Joshua Näf, Stefan Leutenegger
分類: cs.RO
原文アブストラクト
Robust and accurate perception of humans in their 3D scene context is essential for integrating robots into everyday environments. Existing approaches, however, often fail to predict plausible and accurate human motion estimates that are consistent with the surrounding scene, especially in the presence of heavy occlusions or partial visibility. This can limit both safety and efficiency for robotic operations. We introduce HumanFlow, a latent diffusion model that unifies human motion tracking and forecasting, conditioned on the 3D scene context. We show that our human motion model produces smooth and accurate predictions under challenging conditions, including heavy occlusions, and outperforms state-of-the-art methods in tracking accuracy while being significantly more efficient. Furthermore, we show how HumanFlow's latent space can be tightly coupled with control by conditioning a flow-matching-based, approximate MPC policy on these representations. We validate our policy in simulation with real human trajectories for MAV social navigation, demonstrating superior navigation performance and remaining collision-free, even under partial observability of the human.
