Rectified Schrödinger Bridge Matching for Few-Step Visual Navigation
Visual navigation is a core challenge in Embodied AI, requiring autonomous agents to translate high-dimensional sensory observations into continuous, long-horizon action trajectories. While generative policies based on diffusion models and Schrödinger Bridges (SB) effectively capture multimodal action distributions, they require dozens of integration steps due to high-variance stochastic transport, posing a critical barrier for real-time robotic control. We propose Rectified Schrödinger Bridge Matching (RSBM), a framework that exploits a shared velocity-field structure between standard Schrödinger Bridges ($\varepsilon=1$, maximum-entropy transport) and deterministic Optimal Transport ($\...
| Authors | Wuyang Luan, Junhui Li, Weiguang Zhao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 April 2026 |
| Last updated at source | 27 May 2026 |
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