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

2025-07-10

AI Summary

ROS’s distributed architecture and heterogeneous messaging mechanism complicate system state comprehension and raise the barrier to fault diagnosis, exacerbating downtime and maintenance costs. To address this, we propose a generative-AI–driven, user-centered diagnostic framework. Our method integrates multimodal sensor data with ROS runtime messages to enable proactive fault detection and dynamic system-state awareness. We further introduce an adaptive explanation generation model tailored to users across diverse skill levels, incorporating user behavior modeling and real-time feedback to deliver personalized debugging support. Experimental evaluation demonstrates that the framework accurately identifies synthetically injected faults, reduces average diagnosis time by 62%, and significantly improves system availability and non-expert users’ autonomous troubleshooting capability.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Distributed Problem Solving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Bridges gap between complex ROS systems and everyday users
• Reduces ROS error diagnosis difficulty for non-experts
• Minimizes maintenance downtime through proactive error detection

Innovation

• GenAI powered error diagnosis for ROS
• User-centric debugging tools for all levels
• Multimodal data processing for system understanding