Learning-based behavior prediction methods are increasingly being deployed in real-world
autonomous systems, eg, in fleets of self-driving vehicles, which are beginning to …

While autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient, transferable, and …

Deep learning (DL) based methods have been extensively studied for medical image
segmentation, mostly emphasizing the design and training of DL networks. Only few …

To cope with distribution shifts or non-stationarity in system dynamics, online adaptation
algorithms have been introduced to update offline-learned prediction models in real-time …

Human motion prediction is non-trivial in modern industrial settings. Accurate prediction of
human motion can not only improve efficiency in human-robot collaboration, but also …

High fidelity behavior prediction of intelligent agents is critical in many applications, which is
challenging due to the stochasticity, heterogeneity, and time‐varying nature of agent …

Robot decision-making increasingly relies on expressive data-driven human prediction
models when operating around people. While these models are known to suffer from …

In the rapidly evolving field of online test-time adaptation (OTTA), effectively managing
distribution shifts is a pivotal concern. State-of-the-art OTTA methodologies often face …