Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs)
where the parameter posterior is approximated with an inverse formulation of the …

This paper presents a probabilistic framework to obtain both reliable and fast uncertainty
estimates for predictions with Deep Neural Networks (DNNs). Our main contribution is a …

To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution
(OOD) detection capabilities are often required. A powerful approach for OOD detection is …

From robotic space assistance to healthcare robotics, there is increasing interest in robots
that offer adaptable levels of autonomy. In this paper, we propose an action representation …

In robotics, deep learning (DL) methods are used more and more widely, but their general
inability to provide reliable confidence estimates will ultimately lead to fragile and unreliable …

Synthesizing diverse and accurate grasps with multi-fingered hands is an important yet
challenging task in robotics. Previous efforts focusing on generative modeling have fallen …

Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be
introspective on the predicted solutions, ie whether they are feasible or not, to circumvent …

While learning from synthetic training data has recently gained an increased attention, in
real-world robotic applications, there are still performance deficiencies due to the so-called …