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.

Predicting the future trajectories for all vehicles relevant to the ego vehicle is a crucial, yet unsolved challenge to master automated driving. This paper proposes a combination of two lines of research for predicting all the trajectories of a group of vehicles of arbitrary size, considering the mutual interactions possible. Treating the prediction of other vehicles as a planning task for themselves enables the application of the artificial potential field approach. Modeling the driving situation as a potential field turns the trajectory prediction problem back to its original domain – utility space. Humans generate trajectories (that should be predicted) during driving by balancing costs and rewards, which lead to a total utility. The main difficulty inherent to the potential field approach is the hard problem of parameter tuning. Therefore, it is not directly used for prediction. Instead, the potential field representation is used as input for a neural network, which predicts a distribution over trajectories based on distinct maneuvers. This allows a multi-modal prediction for each vehicle and reflects the pattern recognition character.

In this work we present an approach for the optimization of distributed test rigs that are coupled only by information processing. Our application examples are an active suspension system and a linear drive with an active air gap adjustment which both represent a module of the rail-bound vehicle RailCab. Hierarchical optimization is used to combine the module-related optimal operating strategies, which are based on two distinct multiobjective optimizations. A Pareto front of the entire system is presented as a result of the hierarchical optimization.