Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …

Sampling-based methods have become a cornerstone of contemporary approaches to
Model Predictive Control (MPC), as they make no restrictions on the differentiability of the …

The goal of reinforcement learning (RL) is to find a policy that maximizes the expected
cumulative return. It has been shown that this objective can be represented as an …

The future motion of traffic participants is inherently uncertain. To plan safely, therefore, an
autonomous agent must take into account multiple possible trajectory outcomes and …

Normalizing flows have recently emerged as an attractive model for autonomous vehicle
trajectory forecasting. However, a key drawback is that iid samples from flow models often …

Survival analysis, or time-to-event modelling, is a classical statistical problem that has
garnered a lot of interest for its practical use in epidemiology, demographics or actuarial …

Alongside optimization-based planners, sampling-based approaches are often used in
trajectory planning for autonomous driving due to their simplicity. Model predictive path …

Autonomous driving promises various benefits to its users, such as improved comfort, more
ecological transportation, and–most importantly–higher safety than manual driving. One …

A major challenge in robotics is to design robust policies which enable complex and agile
behaviors in the real world. On one end of the spectrum, we have model-free reinforcement …

Time series are widely used in applications such as finance, robotics, telecommunications,
astronomy, and many more. Detecting anomalies like robotic arm failures or server attacks is …