# NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints

arXiv: https://arxiv.org/abs/2605.16979 · HTML: https://mlsanity.com/paper/2605.16979/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2026.

## Built on by (most cited first, 2 of 2)

- [NavPatch: Evidence-Guided Object-Level Costmap Correction with Vision-Language Models](https://mlsanity.com/paper/2609.14543/) (2026)
- [Commonsense-Grounded Path Planning from Abstract Instructions](https://mlsanity.com/paper/2609.22813/) (2026)

## Abstract (arXiv)

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.