ROS Help Desk: GenAI Powered, User-Centric Framework for ROS Error Diagnosis and Debugging

TL;DR

ROS Help Desk is a user-centric, AI-powered framework that simplifies error diagnosis and debugging in ROS-based robotic systems, reducing downtime and enhancing human-robot collaboration.

Contribution

It introduces a novel, adaptive debugging system that provides intuitive error explanations and proactive diagnostics tailored to users with varying expertise.

Findings

Proactively diagnoses errors with high accuracy

Reduces maintenance time significantly

Supports multimodal sensor data integration

Abstract

As the robotics systems increasingly integrate into daily life, from smart home assistants to the new-wave of industrial automation systems (Industry 4.0), there's an increasing need to bridge the gap between complex robotic systems and everyday users. The Robot Operating System (ROS) is a flexible framework often utilised in writing robot software, providing tools and libraries for building complex robotic systems. However, ROS's distributed architecture and technical messaging system create barriers for understanding robot status and diagnosing errors. This gap can lead to extended maintenance downtimes, as users with limited ROS knowledge may struggle to quickly diagnose and resolve system issues. Moreover, this deficit in expertise often delays proactive maintenance and troubleshooting, further increasing the frequency and duration of system interruptions. ROS Help Desk provides intuitive error explanations and debugging support, dynamically customized to users of varying expertise levels. It features user-centric debugging tools that simplify error diagnosis, implements proactive error detection capabilities to reduce downtime, and integrates multimodal data processing for comprehensive system state understanding across multi-sensor data (e.g., lidar, RGB). Testing qualitatively and quantitatively with artificially induced errors demonstrates the system's ability to proactively and accurately diagnose problems, ultimately reducing maintenance time and fostering more effective human-robot collaboration.