Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

This article presents a comprehensive overview of the digital twin technology and its
capability levels, with a specific focus on its applications in the wind energy industry. It …

Multi-objective optimization problems are ubiquitous in robotics, eg, the optimization of a
robot manipulation task requires a joint consideration of grasp pose configurations …

Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

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 …

The two-stage object pose estimation paradigm first detects semantic keypoints on the
image and then estimates the 6D pose by minimizing reprojection errors. Despite performing …

Test-time adaptation methods have been gaining attention recently as a practical solution for
addressing source-to-target domain gaps by gradually updating the model without requiring …

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 …

Optimization problems with nonlinear cost functions and combinatorial constraints appear in
many real-world applications but remain challenging to solve efficiently compared to their …

Deep learning has had remarkable success in robotic perception, but its data-centric nature
suffers when it comes to generalizing to ever-changing environments. By contrast, physics …