FLUX: 整流フローと静的から動的への学習によるクロス身体性生成ナビゲーションポリシーの高速化
FLUX: Accelerating Cross-Embodiment Generative Navigation Policies via Rectified Flow and Static-to-Dynamic Learning
動的ナビゲーションベンチマークDynBenchを導入し、フローベースの統一ナビゲーションポリシーFLUXを提案。直線軌道で推論を高速化し、静的から動的へのカリキュラムで社会的ナビゲーションと静的タスクの堅牢性を向上、ゼロショットで多種ロボットに転移可能。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zeying Gong, Yangyi Zhong, Yiyi Ding, Tianshuai Hu, Guoyang Zhao, Lingdong Kong, Rong Li, Jiadi You, Junwei Liang
分類: cs.RO
原文アブストラクト
Autonomous navigation requires a broad spectrum of skills, from static goal-reaching to dynamic social traversal, yet evaluation remains fragmented across disparate protocols. We introduce DynBench, a dynamic navigation benchmark featuring physically valid crowd simulation. Combined with existing static protocols, it supports comprehensive evaluation across six fundamental navigation tasks. Within this framework, we propose FLUX, the first flow-based unified navigation policy. By linearizing probability flow, FLUX replaces iterative denoising with straight-line trajectories, improving per-step inference efficiency by 47% over prior flow-based methods and 29% over diffusion-based ones. Following a static-to-dynamic curriculum, FLUX initially establishes geometric priors and is subsequently refined through reinforcement learning in dynamic social environments. This regime not only strengthens socially-aware navigation but also enhances static task robustness by capturing recovery behaviors through stochastic action distributions. FLUX achieves state-of-the-art performance across all tasks and demonstrates zero-shot sim-to-real transfer on wheeled, quadrupedal, and humanoid platforms without any fine-tuning.
