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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.16979 |
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| _version_ | 1866910226335137792 |
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| author | Huo, Dongjie Wang, Junhui Gao, Chao Qiao, Yan Zhang, Dong Zhou, Guyue |
| author_facet | Huo, Dongjie Wang, Junhui Gao, Chao Qiao, Yan Zhang, Dong Zhou, Guyue |
| contents | Mobile robots operating in human-centered environments must generate not only collision-free paths but also trajectories that follow local behavioral conventions. Conventional costmap-based navigation emphasizes geometric feasibility and often overlooks such requirements, which can result in socially inappropriate behaviors. This paper presents NORM-Nav, a zero-shot framework that integrates natural language behavioral constraints into costmap-based planning. An LLM parses each instruction into structured constraints and grounds them using real-time vision--LiDAR perception. These constraints are encoded as multi-layer costmaps that represent geometric, semantic, directional, and velocity cues and are directly compatible with standard grid-based planners. Simulation and real-world experiments indicate that NORM-Nav improves task success rates and produces trajectories closer to human references than representative baselines. The project website is available at https://ei-nav.github.io/NORM-Nav. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16979 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints Huo, Dongjie Wang, Junhui Gao, Chao Qiao, Yan Zhang, Dong Zhou, Guyue Robotics Mobile robots operating in human-centered environments must generate not only collision-free paths but also trajectories that follow local behavioral conventions. Conventional costmap-based navigation emphasizes geometric feasibility and often overlooks such requirements, which can result in socially inappropriate behaviors. This paper presents NORM-Nav, a zero-shot framework that integrates natural language behavioral constraints into costmap-based planning. An LLM parses each instruction into structured constraints and grounds them using real-time vision--LiDAR perception. These constraints are encoded as multi-layer costmaps that represent geometric, semantic, directional, and velocity cues and are directly compatible with standard grid-based planners. Simulation and real-world experiments indicate that NORM-Nav improves task success rates and produces trajectories closer to human references than representative baselines. The project website is available at https://ei-nav.github.io/NORM-Nav. |
| title | NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.16979 |