Social Zone as a Barrier Function for Socially-Compliant Robot Navigation

TL;DR

This paper proposes a novel approach to robot navigation that incorporates social norms by deriving social zones from human trajectory data and implementing barrier functions, enabling robots to navigate more naturally and safely around humans.

Contribution

The study introduces a method to encode social norms into robot navigation using barrier functions based on real human trajectory data, enhancing social compliance.

Findings

Effective imitation of human-like navigation behaviors

Versatile and tunable framework for social navigation

Improved safety and efficiency in human-centric environments

Abstract

This study addresses the challenge of integrating social norms into robot navigation, which is essential for ensuring that robots operate safely and efficiently in human-centric environments. Social norms, often unspoken and implicitly understood among people, are difficult to explicitly define and implement in robotic systems. To overcome this, we derive these norms from real human trajectory data, utilizing the comprehensive ATC dataset to identify the minimum social zones humans and robots must respect. These zones are integrated into the robot's navigation system by applying barrier functions, ensuring the robot consistently remains within the designated safety set. Simulation results demonstrate that our system effectively mimics human-like navigation strategies, such as passing on the right side and adjusting speed or pausing in constrained spaces. The proposed framework is versatile, easily comprehensible, and tunable, demonstrating the potential to advance the development of robots designed to navigate effectively in human-centric environments.