no code implementations • ICML 2020 • Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel
As a result, we show that the information form of MND can be scalably applied to represent model uncertainty in MND.
no code implementations • 11 Nov 2023 • Jianxiang Feng, JongSeok Lee, Simon Geisler, Stephan Gunnemann, Rudolph Triebel
To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required.
2 code implementations • 3 Jul 2023 • Jianxiang Feng, Matan Atad, Ismael Rodríguez, Maximilian Durner, Stephan Günnemann, Rudolph Triebel
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i. e. whether they are feasible or not, to circumvent potential efficiency degradation.
2 code implementations • 17 Mar 2023 • Matan Atad, Jianxiang Feng, Ismael Rodríguez, Maximilian Durner, Rudolph Triebel
With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner.
no code implementations • 18 Oct 2022 • JongSeok Lee, Ribin Balachandran, Konstantin Kondak, Andre Coelho, Marco De Stefano, Matthias Humt, Jianxiang Feng, Tamim Asfour, Rudolph Triebel
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments.
no code implementations • 27 Sep 2021 • Jianxiang Feng, Maximilian Durner, Zoltan-Csaba Marton, Ferenc Balint-Benczedi, Rudolph Triebel
This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications.
no code implementations • 23 Sep 2021 • Jianxiang Feng, JongSeok Lee, Maximilian Durner, Rudolph Triebel
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 Sim-to-Real gap.
no code implementations • 20 Sep 2021 • JongSeok Lee, Jianxiang Feng, Matthias Humt, Marcus G. Müller, Rudolph Triebel
This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs).
no code implementations • 7 Jul 2021 • Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, JongSeok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, Xiao Xiang Zhu
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
no code implementations • 20 Jun 2020 • Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel
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 Multivariate Normal Distribution (MND), also known as the information form.