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.

The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for coping with the complex and opaque nature of these architectures. The analysis of performance can be pursued in two ways. On one side, model interpretation techniques aim at "opening the box" to assess the relationship between the input, the inner layers, and the output. For example, saliency and attention models exploit knowledge of the architecture to capture the essential regions of the input that have the most impact on the inference process and output. On the other hand, models can be analysed as "black boxes", e.g., by associating the input samples with extra annotations that do not contribute to model training but can be exploited for characterizing the model response. Such performance-driven meta-annotations enable the detailed characterization of performance metrics and errors and help scientists identify the features of the input responsible for prediction failures and focus their model improvement efforts. This paper presents a structured survey of the tools that support the "black box" analysis of DNNs and discusses the gaps in the current proposals and the relevant future directions in this research field.