FLUX: Accelerating Cross-Embodiment Generative Navigation Policies via Rectified Flow and Static-to-Dynamic Learning

Abstract

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.

Figures (6)

• Figure 1: FLUX: A Flow-Based Unified Policy for Cross-Embodiment Navigation. Our static-to-dynamic training curriculum enables efficient, socially-aware navigation, which transfers zero-shot across three heterogeneous platforms in the real world.
• Figure 2: Overview of FLUX framework.FLUX follows a static-to-dynamic training paradigm. Stage 1 (Top): Given egocentric visual observations and a goal, the flow policy head is pre-trained via imitation learning on static expert trajectories. It generates diverse candidate paths, which are evaluated by the critic head. Stage 2 (Bottom): The framework is post-training using Group Relative Policy Optimization via on-policy rollouts. This stage optimizes for both goal-reaching efficiency and social compliance in dynamic environments.
• Figure 3: Overview of DynBench evaluation suite. Our benchmark provides a unified framework for fundamental navigation. (a) Diverse environments spanning unstructured cluttered environments with randomized obstacles and structured Isaac Sim scenes. (b) Six task modalities categorized into goal-conditioned navigation and coverage maximization, supporting our static-to-dynamic learning paradigm.
• Figure 4: Real-World Cross-Embodiment Deployment.FLUX is deployed on three heterogeneous platforms—wheeled (Go2-W), quadrupedal (Go2), and humanoid (G1)—using only a single RGB-D camera. Despite significant differences in morphology and locomotion, our unified policy achieves robust zero-shot transfer without platform-specific fine-tuning. Critic-guided visualizations further show that safer paths with larger clearance from obstacles receive higher scores, demonstrating reliable risk-aware navigation in real-world scenarios.
• Figure 5: Sensitivity analysis of trajectory number $M$ and sample steps $K$. FLUX outperforms NavDP across nearly all $(M, K)$ configurations, with $M{=}16$, $K{=}6$ achieving near-peak performance.