A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910227752812544 |
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| author | Xu, Kuan Liu, Ruimeng Yang, Yizhuo Liang, Denan Jin, Tongxing Yuan, Shenghai Wang, Chen Xie, Lihua |
| author_facet | Xu, Kuan Liu, Ruimeng Yang, Yizhuo Liang, Denan Jin, Tongxing Yuan, Shenghai Wang, Chen Xie, Lihua |
| contents | Bridging the gap between embodied intelligence and embedded deployment remains a key challenge in intelligent robotic systems, where perception, reasoning, and planning must operate under strict constraints on computation, memory, energy, and real-time execution. In vision-and-language navigation (VLN), existing approaches often face a trade-off between reasoning capability and deployment efficiency on real-world platforms. In this paper, we present a deployable embodied VLN system that achieves both high efficiency and strong high-level reasoning on real-world robots. The system is decomposed into a fast perception-action layer and a deep reasoning layer running asynchronously at different time scales, with a shared memory layer enabling efficient interaction between them. To support long-horizon reasoning, we incrementally construct a compact memory graph and progressively feed decomposed subgraphs into a vision-language model (VLM). Furthermore, we formulate exploration as a Weighted Traveling Repairman Problem (WTRP) by jointly considering reasoning outcomes and the spatial distribution of candidate regions. Extensive experiments in simulation and real-world environments demonstrate improved navigation success and efficiency over existing VLN approaches while maintaining real-time performance on resource-constrained hardware. Code and additional real-world experiments are available at https://github.com/xukuanHIT/HiCo-Nav. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21363 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration Xu, Kuan Liu, Ruimeng Yang, Yizhuo Liang, Denan Jin, Tongxing Yuan, Shenghai Wang, Chen Xie, Lihua Robotics Bridging the gap between embodied intelligence and embedded deployment remains a key challenge in intelligent robotic systems, where perception, reasoning, and planning must operate under strict constraints on computation, memory, energy, and real-time execution. In vision-and-language navigation (VLN), existing approaches often face a trade-off between reasoning capability and deployment efficiency on real-world platforms. In this paper, we present a deployable embodied VLN system that achieves both high efficiency and strong high-level reasoning on real-world robots. The system is decomposed into a fast perception-action layer and a deep reasoning layer running asynchronously at different time scales, with a shared memory layer enabling efficient interaction between them. To support long-horizon reasoning, we incrementally construct a compact memory graph and progressively feed decomposed subgraphs into a vision-language model (VLM). Furthermore, we formulate exploration as a Weighted Traveling Repairman Problem (WTRP) by jointly considering reasoning outcomes and the spatial distribution of candidate regions. Extensive experiments in simulation and real-world environments demonstrate improved navigation success and efficiency over existing VLN approaches while maintaining real-time performance on resource-constrained hardware. Code and additional real-world experiments are available at https://github.com/xukuanHIT/HiCo-Nav. |
| title | A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.21363 |