Learning Social Cost Functions for Human-Aware Path Planning

TL;DR

This paper introduces a method for social robot navigation that learns to recognize social scenarios and adapt its path planning to respect social norms beyond obstacle avoidance, such as queuing and group interactions.

Contribution

It proposes a novel approach to modify traditional path planning cost functions based on learned social norms, enabling more socially aware robot behaviors.

Findings

The method successfully adapts to scenarios like queuing and group interactions.

It learns social norms with a single model, reducing complexity.

The approach maintains robustness of traditional navigation.

Abstract

Achieving social acceptance is one of the main goals of Social Robotic Navigation. Despite this topic has received increasing interest in recent years, most of the research has focused on driving the robotic agent along obstacle-free trajectories, planning around estimates of future human motion to respect personal distances and optimize navigation. However, social interactions in everyday life are also dictated by norms that do not strictly depend on movement, such as when standing at the end of a queue rather than cutting it. In this paper, we propose a novel method to recognize common social scenarios and modify a traditional planner's cost function to adapt to them. This solution enables the robot to carry out different social navigation behaviors that would not arise otherwise, maintaining the robustness of traditional navigation. Our approach allows the robot to learn different social norms with a single learned model, rather than having different modules for each task. As a proof of concept, we consider the tasks of queuing and respect interaction spaces of groups of people talking to one another, but the method can be extended to other human activities that do not involve motion.