What question did this study set out to answer?

The central aim is to evaluate learning-based navigation methods for social robots in human environments.

December 11, 2025Frontiers in Robotics and AIOpen Access

Social robot navigation: a review and benchmarking of learning-based methods

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The central aim is to evaluate learning-based navigation methods for social robots in human environments.
Review of recent progress in learning-based social navigation methods
Introduction of a taxonomy of navigation strategies
Benchmarking existing frameworks in crowd scenarios
Analysis of training environments that facilitate socially compliant behavior
Learning-based methods outperform model-based methods in realistic situations
End-to-end models excel at navigating by using raw sensor data
Identification of trends and challenges in social navigation
Need for improved training environments and evaluation methods

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Implication

This review examines social navigation methods in crowded spaces, suggesting model-based methods need improvement for effective human movement adaptation.

Cite This Study

Alyassi et al. (2025) studied this question.

synapsesocial.com/papers/694019342d562116f28f6fa3 https://doi.org/10.3389/frobt.2025.1658643

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Implication

This review examines social navigation methods in crowded spaces, suggesting model-based methods need improvement for effective human movement adaptation.

Social robot navigation: a review and benchmarking of learning-based methods

Key Points

Discussion

Implication

Cite This Study

Social robot navigation: a review and benchmarking of learning-based methods

Key Points

Discussion

Implication

Cite This Study

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