Abstract Modern manufacturing increasingly relies on robotics to achieve high throughput and quality, especially as production lines become more flexible and parts more customized. Robotic inspection is a critical enabler for quality assurance as it supports repeatable measurements while reducing human workload and variability. Recent vision-language-action (VLA) models have advanced robotic manipulation by integrating visual perception and language understanding for autonomous control. However, the application to robotic inspection, which requires accurate movement without altering the environment, remains underexplored. This work investigates the feasibility of adapting manipulation-pretrained VLA models to an inspection-oriented feature-following task and presents the following contributions: Tailored to the requirements of inspection problems, two approaches for assessing VLA performance are introduced: A trajectory-based evaluation metric to quantify performance in rollouts as well as an action-level metric, useful during the fine-tuning process. In addition, an open-source, manipulation-pretrained VLA model is fine-tuned for a feature-following task. This task represents a simplified 2D inspection setting, designed to capture core aspects of inspection problems encountered in domains such as manufacturing and infrastructure. The model successfully executes these complex feature-following trajectories with competitive performance relative to a human operator in a real-world robotic setup, demonstrating effective transfer from manipulation to this class of inspection tasks. While the study is limited in scale, the results provide initial evidence that VLA models can be extended beyond manipulation to support feature perception and motion generation in automated, robotic inspection. This suggests their potential to support more consistent and automated inspection processes, motivating further investigation into robustness and generalization.

Real-time embedded systems are a significant class of applications, poised to grow even further as automated vehicles and the Internet of Things become a reality. An important problem for these systems is to detect anomalies during operation. Anomaly detection is a form of classification, which can be driven by data collected from the system at execution time. We propose inter-arrival curves as a novel analytic modelling technique for discrete event traces. Our approach relates to the existing technique of arrival curves and expands the technique to anomaly detection. Inter-arrival curves analyze the behaviour of events within a trace by providing upper and lower bounds to their inter-arrival occurrence. We exploit inter-arrival curves in a classification framework that detects deviations within these bounds for anomaly detection. Also, we show how inter-arrival curves act as good features to extract recurrent behaviour that these systems often exhibit. We demonstrate the feasibility and viability of the fully implemented approach with an industrial automotive case study (CAN traces) as well as a deployed aerospace case study (RTOS kernel traces).

Given a set of historic good traces, trace-based anomaly detection deals with the problem of determining whether or not a specific trace represents a normal execution scenario. Most current approaches mainly focus on application areas outside of the embedded systems domain and thus do not take advantage of the intrinsic properties of this domain.