SocRATES: Towards Automated Scenario-based Testing of Social Navigation Algorithms
This work addresses the need for more comprehensive testing of social navigation in robotics, which is crucial for better integration into human environments, though it is incremental as it builds on existing LLM capabilities.
The authors tackled the challenge of labor-intensive scenario creation for evaluating social navigation algorithms by introducing an automated pipeline that generates realistic scenarios using Large Language Models, resulting in significant improvements in scenario translation over naive prompting.
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.