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

TL;DR

NORM-Nav enables mobile robots to follow natural language instructions for socially appropriate navigation by integrating constraints into costmap planning, demonstrated through simulation and real-world tests.

Contribution

It introduces a zero-shot framework that parses natural language constraints and grounds them with perception to improve socially aware robot navigation.

Findings

Increases task success rates in navigation tasks.

Produces trajectories closer to human behavior than baselines.

Works in both simulation and real-world environments.

Abstract

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.