9 accepted papers
Many learning tasks in science and engineering are characterized by sparse datasets, which limits the effectiveness of purely data-driven approaches. At the same time, these problems are often accompanied by rich domain knowledge derived from physical laws, operational requirements, and expert heuri…
Tightly coupled LiDAR–inertial odometry (LIO) systems are critical for autonomous navigation, yet their performance often degrades due to insufficient adaptability to diverse environments and limitations in map representation. To address these limitations, this paper presents GMM-LIO, a robust and a…
Multi-step theorem prediction is a central challenge in automated reasoning. Existing neural–symbolic approaches rely heavily on supervised parametric models, which exhibit limited generalization to evolving theorem libraries. In this work, we explore training-free theorem prediction through the len…
Robust all-weather localization is a critical capability for autonomous systems. While 4D mmWave radar offers superior resilience to adverse environmental conditions compared to LiDAR and cameras, its application in high-precision Simultaneous Localization and Mapping (SLAM) is hindered by significa…
Large Multimodal Models have achieved remarkable progress in integrating vision and language, enabling strong performance across perception, reasoning, and domain-specific tasks. However, their capacity to reason over multiple, visually similar inputs remains insufficiently explored. Such fine-grain…
Deep learning models are increasingly deployed in safety-critical tasks where predictions must satisfy hard constraints, such as physical laws, fairness requirements, or safety limits. However, standard architectures lack built-in mechanisms to enforce such constraints, and existing approaches based…
Detection of deformable linear objects (DLOs) in three-dimensional space is essential for robotic manipulation of DLOs. However, their complex deformations and high degrees of freedom make perception highly susceptible to occlusions, noise, and data missing. To address these challenges, we propose a
Real-world time series often have multiple frequency components that are intertwined with each other, making accurate time series forecasting challenging. Decomposing the mixed frequency components into multiple single frequency components is a natural choice. However, the information density of pat…
Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system. Recent works focus on designing complicated graph neural network architectures to capture shar…