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

Area of Science:

• Robotics
• Artificial Intelligence
• Human-Robot Interaction

Background:

• Effective autonomous mobile robot operation in human environments requires social awareness beyond obstacle avoidance.
• Learning-based methods are advancing the interpretation of human motion and adaptation to social norms for fluid human-robot coexistence.

Purpose of the Study:

• To review recent progress in learning-based social navigation methods for human-robot coexistence.
• To analyze core system components, including training environments and objectives for socially compliant behavior.
• To provide a benchmark of existing frameworks in crowd scenarios and insights into architectural choices.

Main Methods:

• Literature review of learning-based social navigation.
• Taxonomy of navigation methods.
• Benchmarking of existing frameworks in challenging crowd scenarios.
• Analysis of end-to-end models planning from raw sensor input.

Main Results:

• Learning-based approaches frequently outperform model-based methods in realistic coordination tasks.
• End-to-end models demonstrate strong performance by directly planning from sensor data, enabling efficient and adaptive navigation.
• Current frameworks show both advantages and shortcomings in complex crowd navigation.

Conclusions:

• Accurate human movement anticipation, realistic training environments, and robust evaluation methods are crucial for advancing social navigation.
• Overcoming current limitations will facilitate the safe and reliable deployment of social navigation systems in everyday environments.
• Future research should focus on enhancing predictive capabilities, simulation fidelity, and real-world complexity in evaluation.