Researchers from the Max Planck Institute for Intelligent Systems, including Tobin Holtmann, David Stenger, Andres Posada-Moreno, Friedrich Solowjow, and Sebastian Trimpe, have developed a novel approach to state estimation in control and systems engineering. Their work, titled “Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF,” introduces a method that could significantly reduce the manual effort traditionally required for system identification and data collection.
State estimation is a critical task in control and systems engineering, enabling systems to understand their current state based on available data. Traditionally, this process demands extensive manual system identification or data collection to build accurate models. However, the researchers have explored the potential of transformer-based foundation models, which have shown promise in other domains by leveraging pre-trained generalist models to reduce data requirements.
The team introduces the foundation model unscented Kalman filter (FM-UKF), a hybrid approach that combines a transformer-based model of system dynamics with analytically known sensor models using an unscented Kalman filter (UKF). This combination allows the model to generalize across varying dynamics without the need for retraining when encountering new sensor configurations. The UKF is a nonlinear filtering technique that is particularly useful for systems with nonlinear dynamics and measurement models.
To evaluate the effectiveness of their approach, the researchers created a new benchmark of container ship models with complex dynamics. They compared the performance of the FM-UKF against classical methods that rely on approximate system knowledge and an end-to-end approach. The results demonstrated that the FM-UKF offers a competitive trade-off between accuracy, effort, and robustness.
The practical applications of this research for the marine sector are substantial. Accurate state estimation is crucial for the safe and efficient operation of vessels, particularly in complex and dynamic environments. By reducing the need for extensive manual system identification, the FM-UKF could streamline the deployment of state estimation systems on ships, making it easier to adapt to new vessels and sensor configurations. This could lead to improved vessel performance, reduced fuel consumption, and enhanced safety.
Moreover, the researchers have open-sourced the benchmark and dataset used in their study, encouraging further research and development in zero-shot state estimation via foundation models. This collaborative approach could accelerate advancements in the field, benefiting the maritime industry and other sectors that rely on accurate state estimation.
The work of Holtmann, Stenger, Posada-Moreno, Solowjow, and Trimpe represents a significant step forward in the application of foundation models to state estimation. Their innovative approach has the potential to transform how systems are modeled and controlled, paving the way for more efficient and adaptable solutions in the marine sector and beyond. Read the original research paper here.