An Efficient and Multi-Modal Navigation System with One-Step World Model

• URL: http://arxiv.org/abs/2601.12277v1
• Date: Sun, 18 Jan 2026 06:29:56 GMT
• Title: An Efficient and Multi-Modal Navigation System with One-Step World Model
• Abstract summary: Current navigation world models rely on multi-step diffusion processes and autoregressive frame-by-frame generation.<n>We propose a one-step generation paradigm and a 3D U-Net backbone equipped with efficient spatial-temporal attention.<n>This design drastically reduces inference latency, enabling high-frequency control while achieving superior predictive performance.
• Score: 11.582535121562247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Navigation is a fundamental capability for mobile robots. While the current trend is to use learning-based approaches to replace traditional geometry-based methods, existing end-to-end learning-based policies often struggle with 3D spatial reasoning and lack a comprehensive understanding of physical world dynamics. Integrating world models-which predict future observations conditioned on given actions-with iterative optimization planning offers a promising solution due to their capacity for imagination and flexibility. However, current navigation world models, typically built on pure transformer architectures, often rely on multi-step diffusion processes and autoregressive frame-by-frame generation. These mechanisms result in prohibitive computational latency, rendering real-time deployment impossible. To address this bottleneck, we propose a lightweight navigation world model that adopts a one-step generation paradigm and a 3D U-Net backbone equipped with efficient spatial-temporal attention. This design drastically reduces inference latency, enabling high-frequency control while achieving superior predictive performance. We also integrate this model into an optimization-based planning framework utilizing anchor-based initialization to handle multi-modal goal navigation tasks. Extensive closed-loop experiments in both simulation and real-world environments demonstrate our system's superior efficiency and robustness compared to state-of-the-art baselines.

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