no code implementations • 26 Oct 2022 • Julius Ott, Lorenzo Servadei, Gianfranco Mauro, Thomas Stadelmayer, Avik Santra, Robert Wille
There, we show that our method outperforms related Meta-RL approaches on unseen tracking scenarios in peak performance by 16% and the baseline by 35% while detecting OOD data with an F1-Score of 72%.
1 code implementation • 24 Oct 2022 • Julius Ott, Lorenzo Servadei, Jose Arjona-Medina, Enrico Rinaldi, Gianfranco Mauro, Daniela Sánchez Lopera, Michael Stephan, Thomas Stadelmayer, Avik Santra, Robert Wille
This is enabled by the uncertainty estimation of the Q-Value function, which guides the sampling to explore more significant transitions and, thus, learn a more efficient policy.
no code implementations • 30 May 2022 • Michael Stephan, Thomas Stadelmayer, Avik Santra, Georg Fischer, Robert Weigel, Fabian Lurz
This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continous wave radar.
no code implementations • 22 Nov 2021 • Thomas Stadelmayer, Avik Santra, Robert Weigel, Fabian Lurz
The paper proposes a novel feature extraction approach for FMCW radar systems in the field of short-range gesture sensing.
1 code implementation • 12 Oct 2021 • Lorenzo Servadei, Huawei Sun, Julius Ott, Michael Stephan, Souvik Hazra, Thomas Stadelmayer, Daniela Sanchez Lopera, Robert Wille, Avik Santra
In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function.