SocRATES: Towards Automated Scenario-based Testing of Social Navigation Algorithms

TL;DR

This paper introduces an automated pipeline leveraging large language models to generate realistic, scenario-based social navigation tests, enabling more comprehensive evaluation of robot social competence in human environments.

Contribution

It presents a novel, automated method for creating detailed social navigation scenarios using LLMs, reducing manual effort and enhancing evaluation realism.

Findings

Pipeline produces realistic scenarios

Significantly improves scenario translation quality

Initial positive feedback from social navigation experts

Abstract

Current social navigation methods and benchmarks primarily focus on proxemics and task efficiency. While these factors are important, qualitative aspects such as perceptions of a robot's social competence are equally crucial for successful adoption and integration into human environments. We propose a more comprehensive evaluation of social navigation through scenario-based testing, where specific human-robot interaction scenarios can reveal key robot behaviors. However, creating such scenarios is often labor-intensive and complex. In this work, we address this challenge by introducing a pipeline that automates the generation of context-, and location-appropriate social navigation scenarios, ready for simulation. Our pipeline transforms simple scenario metadata into detailed textual scenarios, infers pedestrian and robot trajectories, and simulates pedestrian behaviors, which enables more controlled evaluation. We leverage the social reasoning and code-generation capabilities of Large Language Models (LLMs) to streamline scenario generation and translation. Our experiments show that our pipeline produces realistic scenarios and significantly improves scenario translation over naive LLM prompting. Additionally, we present initial feedback from a usability study with social navigation experts and a case-study demonstrating a scenario-based evaluation of three navigation algorithms.