  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' 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.
