Over the last decade, the use of autonomous drone systems for surveying, search and
rescue, or last-mile delivery has increased exponentially. With the rise of these applications …

While neural rendering has demonstrated impressive capabilities in 3D scene
reconstruction and novel view synthesis, it heavily relies on high-quality sharp images and …

An open research question in robotics is how to combine the benefits of model-free
reinforcement learning (RL)—known for its strong task performance and flexibility in …

Accurate and robust localization and mapping are essential components for most
autonomous robots. In this paper, we propose a SLAM system for building globally …

Simultaneous Localization and Mapping (SLAM) stands as one of the critical challenges in
robot navigation. A SLAM system often consists of a front-end component for motion …

Deep Equilibrium (DEQ) Models, an emerging class of implicit models that maps inputs to
fixed points of neural networks, are of growing interest in the deep learning community …

Data-driven methods such as reinforcement and imitation learning have achieved
remarkable success in robot autonomy. However, their data-centric nature still hinders them …

This paper explores the problem of collision-free motion generation for manipulators by
formulating it as a global motion optimization problem. We develop a parallel optimization …

Underwater robotic surveys can be costly due to the complex working environment and the
need for various sensor modalities. While underwater simulators are essential, many …

Deep learning has the potential to enhance both the performance and efficiency of indoor
positioning using inertial measurement units (IMUs). Many existing deep learning methods …