How Human Motion Prediction Quality Shapes Social Robot Navigation Performance in Constrained Spaces

2026-01-14

AI Summary

This study addresses the challenge of balancing human safety, comfort, and efficiency in spatially constrained dynamic environments, where human motion exhibits high stochasticity, substantial inter-individual variability, and limited available data. Through a user study involving 80 participants across two geographic regions and two robotic platforms, the authors systematically evaluate how the quality of human motion prediction influences robot navigation performance, human task efficiency, and subjective experience. The findings reveal that conventional metrics such as average displacement error fail to reliably capture real-world human–robot interaction outcomes; in confined spaces, humans often do not reciprocate the robot’s cooperative behaviors; and improving robotic efficiency can inadvertently compromise human efficiency and comfort. By integrating real-world experiments, multi-platform deployment, and mixed-method evaluation, this work elucidates the interplay between prediction accuracy and navigation strategy, offering empirical insights and key design principles for socially aware robot behavior in complex environments.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Human-Robot Interaction
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Motivated by the vision of integrating mobile robots closer to humans in warehouses, hospitals, manufacturing plants, and the home, we focus on robot navigation in dynamic and spatially constrained environments. Ensuring human safety, comfort, and efficiency in such settings requires that robots are endowed with a model of how humans move around them. Human motion prediction around robots is especially challenging due to the stochasticity of human behavior, differences in user preferences, and data scarcity. In this work, we perform a methodical investigation of the effects of human motion prediction quality on robot navigation performance, as well as human productivity and impressions. We design a scenario involving robot navigation among two human subjects in a constrained workspace and instantiate it in a user study ($N=80$) involving two different robot platforms, conducted across two sites from different world regions. Key findings include evidence that: 1) the widely adopted average displacement error is not a reliable predictor of robot navigation performance and human impressions; 2) the common assumption of human cooperation breaks down in constrained environments, with users often not reciprocating robot cooperation, and causing performance degradations; 3) more efficient robot navigation often comes at the expense of human efficiency and comfort.

Problem

• human motion prediction
• social robot navigation
• constrained environments
• human-robot interaction
• navigation performance

Innovation

• human motion prediction
• social robot navigation
• constrained environments
• human-robot interaction
• navigation performance evaluation