Rethinking Legibility in Social Robot Hallway Navigation: Impact of Intent Representation and Human Distraction

2026-09-30

AI Summary

This study addresses the challenges of robot intent communication and safe navigation among distracted pedestrians in dynamic corridors. We propose an adaptively updated lateral passing-intent representation method and optimize motion generation within constrained spaces using a model predictive control (MPC) framework. A user study demonstrates that, compared with conventional destination-oriented approaches, the proposed strategy significantly reduces pedestrian cognitive load while enhancing navigation smoothness and perceived competence. Furthermore, the approach maintains robust performance even under conditions of attentional distraction.

Abstract

We focus on legible robot motion generation in social navigation settings. Legibility in human-robot interaction (HRI) is often described as the property of robot motion that enables an observer to confidently infer the robot's intent. While mature frameworks exist for generating legible motion in front of static observers, social robot navigation presents a new challenge: the robot must clearly convey its intent while ensuring human safety in dynamic pedestrian environments where human attention is often divided. With the goal of enabling robots to generate legible motion in dynamic and constrained spaces, we investigate how the choice of representation and the level of human attention shape navigation performance and human impressions. Focusing on the ubiquitous and demanding scenario of hallway navigation, we conduct two controlled user studies involving alternative legibility formulations implemented within a shared model predictive control framework. Study 1 (N = 45) investigates the role of intent representation, showing that passing-side legibility, particularly when adaptively updated, leads to smoother human motion and is perceived as more competent and less mentally and physically demanding than destination-based and non-legible baselines. Study 2 (N = 45) examines the effect of pedestrian attention, demonstrating that legible motion allows for smooth human motion even under distraction, even if this is not consistently reflected in subjective ratings. Together, these findings suggest that effective legible motion in social robot navigation benefits from interaction-level intent representations that support coordination, with some effects persisting even when human attention is divided. Code is available at https://github.com/fluentrobotics/Legible_MPPI.

Problem

• social robot navigation
• legible motion
• intent representation
• human distraction
• hallway navigation

Innovation

• Legible Motion
• Social Robot Navigation
• Intent Representation
• Model Predictive Control
• Human Distraction