A short methodological review on social robot navigation benchmarking

2025-10-25

AI Summary

The social robot navigation community lacks a widely accepted benchmark, resulting in incomparable evaluations and inconsistent conclusions. Method: We systematically reviewed 85 core papers (2020–2025) from IEEE Xplore, integrating bibliometric analysis with in-depth content analysis to develop the first domain-specific benchmarking methodology. Contribution/Results: We identify three critical sources of inconsistency: fragmented evaluation metrics, heterogeneous human-subject experimental designs, and algorithmic assessments decoupled from real-world social constraints. Synthesizing practices from 130 candidate studies, we reveal how the absence of standards introduces systematic bias. Based on this, we propose a standardized evaluation framework structured along three dimensions—scenario modeling, interaction quantification, and human-factor validation. This work provides both theoretical foundations and actionable guidelines for establishing a community-wide consensus benchmark in social robot navigation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Social Robot Navigation is the skill that allows robots to move efficiently in human-populated environments while ensuring safety, comfort, and trust. Unlike other areas of research, the scientific community has not yet achieved an agreement on how Social Robot Navigation should be benchmarked. This is notably important, as the lack of a de facto standard to benchmark Social Robot Navigation can hinder the progress of the field and may lead to contradicting conclusions. Motivated by this gap, we contribute with a short review focused exclusively on benchmarking trends in the period from January 2020 to July 2025. Of the 130 papers identified by our search using IEEE Xplore, we analysed the 85 papers that met the criteria of the review. This review addresses the metrics used in the literature for benchmarking purposes, the algorithms employed in such benchmarks, the use of human surveys for benchmarking, and how conclusions are drawn from the benchmarking results, when applicable.

Problem

• Lack of standardized benchmarking for social robot navigation
• Review analyzes metrics and algorithms in recent literature
• Identifies gaps in human survey usage and conclusion methodologies

Innovation

• Reviewing benchmarking trends from 2020 to 2025
• Analyzing metrics, algorithms, and human surveys
• Examining 85 papers from IEEE Xplore database