no code implementations • pproximateinference AABI Symposium 2019 • Petra Poklukar, Judith Bütepage, Danica Kragic
Recent findings show that deep generative models can judge out-of-distribution samples as more likely than those drawn from the same distribution as the training data.
no code implementations • 14 Oct 2019 • Judith Bütepage, Ali Ghadirzadeh, Özge Öztimur Karadag, Mårten Björkman, Danica Kragic
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals.
1 code implementation • 24 Sep 2018 • Judith Bütepage, Hedvig Kjellström, Danica Kragic
Therefore, video-based human activity modeling is concerned with a number of tasks such as inferring current and future semantic labels, predicting future continuous observations as well as imagining possible future label and feature sequences.
no code implementations • 19 Sep 2018 • Judith Bütepage, Danica Kragic
In this work we introduce semi-supervised variational recurrent neural networks which are able to a) model temporal distributions over latent factors and the observable feature space, b) incorporate discrete labels such as activity type when available, and c) generate possible future action sequences on both feature and label level.
no code implementations • 27 Feb 2017 • Judith Bütepage, Hedvig Kjellström, Danica Kragic
Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions.
no code implementations • CVPR 2017 • Judith Bütepage, Michael Black, Danica Kragic, Hedvig Kjellström
To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units.
no code implementations • 27 Jul 2016 • Ali Ghadirzadeh, Judith Bütepage, Atsuto Maki, Danica Kragic, Mårten Björkman
Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior.