Out-of-distribution (OoD) detectors work alongside deep neural networks (DNNs) to reduce their risks in eliciting wrong predictions. Unfortunately, OoD detectors built on data-centric designs are also subject to robustness issues, as the DNNs. This paper examines the practical robustness of OoD detectors, taking computer vision tasks as examples and considering natural input perturbations that may come from camera positions and lighting conditions. Our study incorporates extensive experiments over 2000+ settings and correlation studies, highlighting significant challenges in OoD detection robustness, e.g., OoD detectors’ robustness error rate in practical settings can be as high as 28%. The paper advances our understanding of OoD detectors’ applicability in real world and the interplay of their robustness with DNNs’ robustness, calling for novel methodology to design robust OoD detectors in broader signal processing tasks.

As marine exploration becomes increasingly important, marine robots have been extensively studied in recent years. Despite some well-designed robots have already achieved to various successful missions, most existing robots struggle to adapt to diverse demands or tasks due to their fixed structure and complexity of the marine environment. To address these challenges, we present a novel reconfigurable marine robot named Sea-U-Whale. This system can dynamically adjust its actuator configuration in the marine environment, providing superior environmental adaptability, maneuverability, and ver-satile mobility. Considering the demands of unmanned ocean exploration, an active reconfiguration mechanism and three distinct vehicle modes are designed for optimal actuation in various marine scenarios. The multi-modal mobility of our system and its robust performance have been validated through extensive field tests and water tank experiments, demonstrating its potential in handling a wide range of mission profiles.

Autonomous Marine Vehicles (AMVs) have been widely used in many critical tasks such as surveillance, patrolling, marine environment monitoring, and hydrographic surveying. However, most typical AMVs cannot meet the diverse demands of different marine tasks. In this article, we design a new type of remote-controlled hydrofoil marine vehicle, named Sea-U-Foil, which is suitable for different marine scenarios. Sea-U-Foil features three distinct locomotion modes, displacement mode, foilborne mode, and submarine mode, which enable the platform flexible mobility, high-speed and high-load capacities, and superior concealment. Specifically, the submarine mode makes Sea-U-Foil unique among previous studies. In addition, the performance of Sea-U-Foil in foilborne mode outperforms those of most current unmanned surface vehicles (USVs) in terms of speed and payload. To the best of our knowledge, we are the first to introduce a new type of AMV that can work in displacement mode, foilborne mode, and submarine mode. We elaborate on the design principles and methodologies of Sea-U-Foil first, then validate the effectiveness of its tri-modal locomotion through extensive experiments.